Recent advances on soft computing and data mining herawan, ghazali deris 2014 05 30

Advances in Intelligent Systems and Computing 287

Tutut Herawan
Rozaida Ghazali
Mustafa Mat Deris Editors

Recent Advances
on Soft Computing
and Data Mining
Proceedings of the First International
Conference on Soft Computing
and Data Mining (SCDM-2014)
Universiti Tun Hussein Onn Malaysia
Johor, Malaysia June 16th–18th, 2014


Advances in Intelligent Systems and Computing
Volume 287

Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail:

For further volumes:
/>

About this Series
The series “Advances in Intelligent Systems and Computing” contains publications on theory,
applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all
disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing.
The publications within “Advances in Intelligent Systems and Computing” are primarily

textbooks and proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An
important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results.

Advisory Board
Chairman
Nikhil R. Pal, Indian Statistical Institute, Kolkata, India
e-mail:
Members
Rafael Bello, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, Cuba
e-mail:
Emilio S. Corchado, University of Salamanca, Salamanca, Spain
e-mail:
Hani Hagras, University of Essex, Colchester, UK
e-mail:
László T. Kóczy, Széchenyi István University, Gy˝or, Hungary
e-mail:
Vladik Kreinovich, University of Texas at El Paso, El Paso, USA
e-mail:
Chin-Teng Lin, National Chiao Tung University, Hsinchu, Taiwan
e-mail:
Jie Lu, University of Technology, Sydney, Australia
e-mail:
Patricia Melin, Tijuana Institute of Technology, Tijuana, Mexico
e-mail:
Nadia Nedjah, State University of Rio de Janeiro, Rio de Janeiro, Brazil
e-mail:
Ngoc Thanh Nguyen, Wroclaw University of Technology, Wroclaw, Poland
e-mail:
Jun Wang, The Chinese University of Hong Kong, Shatin, Hong Kong
e-mail:

Tutut Herawan · Rozaida Ghazali
Mustafa Mat Deris
Editors

Recent Advances on Soft
Computing and Data Mining
Proceedings of the First International
Conference on Soft Computing and
Data Mining (SCDM-2014)
Universiti Tun Hussein Onn Malaysia, Johor,
Malaysia June, 16th–18th, 2014

ABC


Editors
Tutut Herawan
Faculty of Computer Science and
Information Technology
University of Malaya
Kuala Lumpur
Malaysia

Mustafa Mat Deris
Faculty of Computer Science and
Information Technology
Universiti Tun Hussein Onn Malaysia
Malaysia

Rozaida Ghazali
Faculty of Computer Science and
Information Technology
Universiti Tun Hussein Onn Malaysia
Malaysia

ISSN 2194-5357
ISBN 978-3-319-07691-1
DOI 10.1007/978-3-319-07692-8

ISSN 2194-5365 (electronic)
ISBN 978-3-319-07692-8 (eBook)

Springer Cham Heidelberg New York Dordrecht London
Library of Congress Control Number: 2014940281
c Springer International Publishing Switzerland 2014
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the
material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage
and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known
or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews
or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a
computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts
thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be
obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under
the respective Copyright Law.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication
does not imply, even in the absence of a specific statement, that such names are exempt from the relevant
protective laws and regulations and therefore free for general use.
While the advice and information in this book are believed to be true and accurate at the date of publication,

neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or
omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material
contained herein.

Printed on acid-free paper
Springer is part of Springer Science+Business Media (www.springer.com)


Preface

We are honored to be part of this special event in the First International Conference on
Soft Computing and Data Mining (SCDM-2014). SCDM-2014 will be held at Universiti
Tun Hussein Onn Malaysia, Johor, Malaysia on June 16th –18th, 2014. It has attracted
145 papers from 16 countries from all over the world. Each paper was peer reviewed by
at least two members of the Program Committee. Finally, only 65 (44%) papers with
the highest quality were accepted for oral presentation and publication in these volume
proceedings.
The papers in these proceedings are grouped into two sections and two in conjunction
workshops:
•
•
•
•

Soft Computing
Data Mining
Workshop on Nature Inspired Computing and Its Applications
Workshop on Machine Learning for Big Data Computing

On behalf of SCDM-2014, we would like to express our highest gratitude to be given

the chance to cooperate with Applied Mathematics and Computer Science Research
Centre, Indonesia and Software and Multimedia Centre, Universiti Tun Hussein Onn
Malaysia for their support. Our special thanks go to the Vice Chancellor of Universiti
Tun Hussein Onn Malaysia, Steering Committee, General Chairs, Program Committee
Chairs, Organizing Chairs, Workshop Chairs, all Program and Reviewer Committee
members for their valuable efforts in the review process that helped us to guarantee the
highest quality of the selected papers for the conference.
We also would like to express our thanks to the four keynote speakers, Prof. Dr.
Nikola Kasabov from KEDRI, Auckland University of Technology, New Zealand; Prof.
Dr. Hamido Fujita from Iwate Prefectural University (IPU); Japan, Prof. Dr. Hojjat
Adeli from The Ohio State University; and Prof. Dr. Mustafa Mat Deris from SCDM,
Universiti Tun Hussein Onn Malaysia.
Our special thanks are due also to Prof. Dr. Janusz Kacprzyk and Dr. Thomas
Ditzinger for publishing the proceeding in Advanced in Intelligent and Soft Computing
of Springer. We wish to thank the members of the Organizing and Student Committees
for their very substantial work, especially those who played essential roles.


VI

Preface

We cordially thank all the authors for their valuable contributions and other participants of this conference. The conference would not have been possible without them.
Editors
Tutut Herawan
Rozaida Ghazali
Mustafa Mat Deris


Conference Organization

Patron
Prof. Dato’ Dr. Mohd Noh
Bin Dalimin

Vice-Chancellor of Universiti Tun Hussein Onn
Malaysia

Honorary Chair
Witold Pedrycz
Junzo Watada
Ajith Abraham
A. Fazel Famili
Hamido Fujita

University of Alberta, Canada
Waseda University, Japan
Machine Intelligence Research Labs, USA
National Research Council of Canada
Iwate Prefectural University, Japan

Steering Committee
Nazri Mohd Nawi
Jemal H. Abawajy

Universiti Tun Hussein Onn Malaysia, UTHM
Deakin University, Australia

Chair
Rozaida Ghazali

Tutut Herawan
Mustafa Mat Deris

Universiti Tun Hussein Onn Malaysia
Universiti Malaya
Universiti Tun Hussein Onn Malaysia

Secretary
Noraini Ibrahim
Norhalina Senan

Universiti Tun Hussein Onn Malaysia
Universiti Tun Hussein Onn Malaysia

Organizing Committee
Hairulnizam Mahdin
Suriawati Suparjoh

Universiti Tun Hussein Onn Malaysia
Universiti Tun Hussein Onn Malaysia


VIII

Conference Organization

Rosziati Ibrahim
Mohd. Hatta b. Mohd.
Ali @ Md. Hani
Nureize Arbaiy

Noorhaniza Wahid
Mohd Najib Mohd Salleh
Rathiah Hashim

Universiti Tun Hussein Onn Malaysia
Universiti Tun Hussein Onn Malaysia
Universiti Tun Hussein Onn Malaysia
Universiti Tun Hussein Onn Malaysia
Universiti Tun Hussein Onn Malaysia
Universiti Tun Hussein Onn Malaysia

Program Committee Chair
Mohd Farhan Md Fudzee
Shahreen Kassim

Universiti Tun Hussein Onn Malaysia
Universiti Tun Hussein Onn Malaysia

Proceeding Chair
Tutut Herawan
Rozaida Ghazali
Mustafa Mat Deris

Universiti Malaya
Universiti Tun Hussein Onn Malaysia
Universiti Tun Hussein Onn Malaysia

Workshop Chair
Prima Vitasari
Noraziah Ahmad

Institut Teknologi Nasional, Indonesia
Universiti Malaysia Pahang

Program Committee
Soft Computing
Abir Jaafar Hussain
Adel Al-Jumaily
Ali Selamat
Anca Ralescu
Azizul Azhar Ramli
Dariusz Krol
Dhiya Al-Jumeily
Ian M. Thornton
Iwan Tri Riyadi Yanto
Jan Platos
Jon Timmis
Kai Meng Tay
Lim Chee Peng
Ma Xiuqin
Mamta Rani

Liverpool John Moores University, UK
University of Technology, Sydney
Universiti Teknologi Malaysia
University of Cincinnati, USA
Universiti Tun Hussein Onn Malaysia
Wroclaw University, Poland
Liverpool John Moores University, UK
University of Swansea, UK

Universitas Ahmad Dahlan, Indonesia
VSB-Technical University of Ostrava
University of York Heslington, UK
UNIMAS
Deakin University
Northwest Normal University, PR China
Krishna Engineering College, India


Conference Organization

Meghana R. Ransing
Muh Fadel Jamil Klaib
Mohd Najib Mohd Salleh
Mustafa Mat Deris
Natthakan Iam-On
Nazri Mohd Nawi
Qin Hongwu
R.B. Fajriya Hakim
Rajesh S. Ransing
Richard Jensen
Rosziati Ibrahim
Rozaida Ghazali
Russel Pears
Safaai Deris
Salwani Abdullah
Shamshul Bahar Yaakob
Siti Mariyam Shamsuddin
Siti Zaiton M. Hashim
Theresa Beaubouef

Tutut Herawan
Yusuke Nojima

University of Swansea, UK
Jadara University, Jordan
Universiti Tun Hussein Onn Malaysia
Universiti Tun Hussein Onn Malaysia
Mae Fah Luang University, Thailand
Universiti Tun Hussein Onn Malaysia
Northwest Normal University, PR China
Universitas Islam Indonesia
University of Swansea, UK
Aberystwyth University
Universiti Tun Hussein Onn Malaysia
Universiti Tun Hussein Onn Malaysia
Auckland University of Technology
Universiti Teknologi Malaysia
Universiti Kebangsaan Malaysia
UNIMAP
Universiti Teknologi Malaysia
Universiti Teknologi Malaysia
Southeastern Louisiana University
Universiti Malaya
Osaka Prefecture University

Data Mining
Ali Mamat
Bac Le
Bay Vo
Beniamino Murgante

David Taniar
Eric Pardede
George Coghill
Hamidah Ibrahim
Ildar Batyrshin
Jemal H. Abawajy
Kamaruddin Malik Mohamad
La Mei Yan
Md Anisur Rahman
Md Yazid Md Saman
Mohd Hasan Selamat
Naoki Fukuta
Noraziah Ahmad
Norwati Mustapha
Patrice Boursier

Universiti Putra Malaysia
University of Science, Ho Chi Minh City,
Vietnam
Ho Chi Minh City University of Technology,
Vietnam
University of Basilicata, Italy
Monash University
La Trobe University
University of Auckland
Universiti Putra Malaysia
Mexican Petroleum Institute
Deakin University
Universiti Tun Hussein Onn Malaysia
ZhuZhou Institute of Technology, PR China

Charles Sturt University, Australia
Universiti Malaysia Terengganu
Universiti Putra Malaysia
Shizuoka University
Universiti Malaysia Pahang
Universiti Putra Malaysia
University of La Rochelle, France

IX


X

Conference Organization

Prabhat K. Mahanti
Roslina Mohd Sidek
Palaiahnakote Shivakumara
Patricia Anthony
Sofian Maabout
Shuliang Wang
Tetsuya Yoshida
Vera Yuk Ying Chung
Wan Maseri Wan Mohd
Wenny Rahayu
Yingjie Hu
You Wei Yuan
Zailani Abdullah

University of New Brunswick, Canada

Universiti Malaysia Pahang
Universiti Malaya
Lincoln University, New Zealand
Université Bordeaux, France
Wuhan University
Hokkaido University
University of Sydney
Universiti Malaysia Pahang
La Trobe University
Auckland University of Technology
ZhuZhou Institute of Technology, PR China
Universiti Malaysia Terengganu

Workshop on Nature Inspired Computing and Its Applications
Somnuk
Phon-Amnuaisuk (Chair)
Adham Atyabi
Ak Hj Azhan Pg Hj Ahmad
Atikom Ruekbutra
Mahanakorn
Au Thien Wan
Hj Idham M. Hj Mashud
Hj Rudy Erwan bin Hj Ramlie
Ibrahim Edris
Khor Kok Chin
Ng Keng Hoong
Somnuk Phon-Amnuaisuk
Ting Choo Yee
Werasak Kurutach

Institut Teknologi Brunei
Flinders University
Institut Teknologi Brunei
University of Technology
Institut Teknologi Brunei
Institut Teknologi Brunei
Institut Teknologi Brunei
Institut Teknologi Brunei
Multimedia University
Multimedia University
Institut Teknologi Brunei
Multimedia University
Mahanakorn University of Technology

Workshop on Machine Learning for Big Data Computing
Norbahiah Ahmad (Chair)
Siti Mariyam Shamsuddin

Universiti Teknologi Malaysia
Universiti Teknologi Malaysia


Contents

Soft Computing Track
A Fuzzy Time Series Model in Road Accidents Forecast . . . . . . . . . . . . . . . . .
Lazim Abdullah, Chye Ling Gan
A Jordan Pi-Sigma Neural Network for Temperature Forecasting in Batu
Pahat Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Noor Aida Husaini, Rozaida Ghazali, Lokman Hakim Ismail,

Tutut Herawan
A Legendre Approximation for Solving a Fuzzy Fractional Drug
Transduction Model into the Bloodstream . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ali Ahmadian, Norazak Senu, Farhad Larki, Soheil Salahshour,
Mohamed Suleiman, Md. Shabiul Islam
A Multi-reference Ontology for Profiling Scholars’ Background
Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Bahram Amini, Roliana Ibrahim, Mohd Shahizan Othman,
Mohd Nazir Ahmad
A New Binary Particle Swarm Optimization for Feature Subset Selection
with Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Amir Rajabi Behjat, Aida Mustapha, Hossein Nezamabadi-Pour,
Md. Nasir Sulaiman, Norwati Mustapha

1

11

25

35

47

A New Hybrid Algorithm for Document Clustering Based on Cuckoo
Search and K-means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ishak Boushaki Saida, Nadjet Kamel, Bendjeghaba Omar

59

A New Positive and Negative Linguistic Variable of Interval Triangular
Type-2 Fuzzy Sets for MCDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nurnadiah Zamri, Lazim Abdullah

69


XII

Contents

A New Qualitative Evaluation for an Integrated Interval Type-2 Fuzzy
TOPSIS and MCGP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nurnadiah Zamri, Lazim Abdullah
A Performance Comparison of Genetic Algorithm’s Mutation
Operators in n-Cities Open Loop Travelling Salesman
Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hock Hung Chieng, Noorhaniza Wahid
A Practical Weather Forecasting for Air Traffic Control System Using
Fuzzy Hierarchical Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Azizul Azhar Ramli, Mohammad Rabiul Islam, Mohd Farhan Md Fudzee,
Mohamad Aizi Salamat, Shahreen Kasim

79

89

99

Adapted Bio-inspired Artificial Bee Colony and Differential Evolution for

Feature Selection in Biomarker Discovery Analysis . . . . . . . . . . . . . . . . . . . . . 111
Syarifah Adilah Mohamed Yusoff, Rosni Abdullah, Ibrahim Venkat
An Artificial Intelligence Technique for Prevent Black Hole Attacks in
MANET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Khalil I. Ghathwan, Abdul Razak B. Yaakub
ANFIS Based Model for Bispectral Index Prediction . . . . . . . . . . . . . . . . . . . . 133
Jing Jing Chang, S. Syafiie, Raja Kamil Raja Ahmad,
Thiam Aun Lim
Classify a Protein Domain Using SVM Sigmoid Kernel . . . . . . . . . . . . . . . . . . 143
Ummi Kalsum Hassan, Nazri Mohd Nawi, Shahreen Kasim,
Azizul Azhar Ramli, Mohd Farhan Md Fudzee, Mohamad Aizi
Salamat
Color Histogram and First Order Statistics for Content Based Image
Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
Muhammad Imran, Rathiah Hashim, Noor Eliza Abd Khalid
Comparing Performances of Cuckoo Search Based Neural Networks . . . . . . 163
Nazri Mohd Nawi, Abdullah Khan, M.Z. Rehman, Tutut Herawan,
Mustafa Mat Deris
CSLMEN: A New Cuckoo Search Levenberg Marquardt Elman Network
for Data Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
Nazri Mohd Nawi, Abdullah Khan, M.Z. Rehman, Tutut Herawan,
Mustafa Mat Deris
Enhanced MWO Training Algorithm to Improve Classification Accuracy
of Artificial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
Ahmed A. Abusnaina, Rosni Abdullah, Ali Kattan


Contents

XIII

Fuzzy Modified Great Deluge Algorithm for Attribute Reduction . . . . . . . . . 195
Majdi Mafarja, Salwani Abdullah
Fuzzy Random Regression to Improve Coefficient Determination in
Fuzzy Random Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
Nureize Arbaiy, Hamijah Mohd Rahman
Honey Bees Inspired Learning Algorithm: Nature Intelligence Can
Predict Natural Disaster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Habib Shah, Rozaida Ghazali, Yana Mazwin Mohmad Hassim
Hybrid Radial Basis Function with Particle Swarm Optimisation
Algorithm for Time Series Prediction Problems . . . . . . . . . . . . . . . . . . . . . . . . 227
Ali Hassan, Salwani Abdullah
Implementation of Modified Cuckoo Search Algorithm on Functional
Link Neural Network for Climate Change Prediction via Temperature
and Ozone Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
Siti Zulaikha Abu Bakar, Rozaida Ghazali, Lokman Hakim Ismail,
Tutut Herawan, Ayodele Lasisi
Improving Weighted Fuzzy Decision Tree for Uncertain Data
Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249
Mohd Najib Mohd Salleh
Investigating Rendering Speed and Download Rate of Three-Dimension
(3D) Mobile Map Intended for Navigation Aid Using Genetic Algorithm . . . 261
Adamu I. Abubakar, Akram Zeki, Haruna Chiroma,
Tutut Herawan
Kernel Functions for the Support Vector Machine: Comparing
Performances on Crude Oil Price Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
Haruna Chiroma, Sameem Abdulkareem, Adamu I. Abubakar,
Tutut Herawan
Modified Tournament Harmony Search for Unconstrained Optimisation
Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283

Moh’d Khaled Shambour, Ahamad Tajudin Khader,
Ahmed A. Abusnaina, Qusai Shambour
Multi-objective Particle Swarm Optimization for Optimal Planning of
Biodiesel Supply Chain in Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293
Maryam Valizadeh, S. Syafiie, I.S. Ahamad
Nonlinear Dynamics as a Part of Soft Computing Systems: Novel
Approach to Design of Data Mining Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 303
Elena N. Benderskaya


XIV

Contents

Soft Solution of Soft Set Theory for Recommendation in Decision
Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
R.B. Fajriya Hakim, Eka Novita Sari, Tutut Herawan
Two-Echelon Logistic Model Based on Game Theory with Fuzzy
Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325
Pei Chun Lin, Arbaiy Nureize

Data Mining Track
A Hybrid Approach to Modelling the Climate Change
Effects on Malaysia’s Oil Palm Yield at the Regional
Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335
Subana Shanmuganathan, Ajit Narayanan, Maryati Mohamed,
Rosziati Ibrahim, Haron Khalid
A New Algorithm for Incremental Web Page Clustering Based on
k-Means and Ant Colony Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347
Yasmina Boughachiche, Nadjet Kamel

A Qualitative Evaluation of Random Forest Feature Learning . . . . . . . . . . . . 359
Adelina Tang, Joan Tack Foong
A Semantic Content-Based Forum Recommender System Architecture
Based on Content-Based Filtering and Latent Semantic Analysis . . . . . . . . . . 369
Naji Ahmad Albatayneh, Khairil Imran Ghauth, Fang-Fang Chua
A Simplified Malaysian Vehicle Plate Number
Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379
Abd Kadir Mahamad, Sharifah Saon, Sarah Nurul Oyun Abdul Aziz
Agglomerative Hierarchical Co-clustering Based on Bregman
Divergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389
Guowei Shen, Wu Yang, Wei Wang, Miao Yu, Guozhong Dong
Agreement between Crowdsourced Workers and Expert Assessors in
Making Relevance Judgment for System Based IR Evaluation . . . . . . . . . . . . 399
Parnia Samimi, Sri Devi Ravana
An Effective Location-Based Information Filtering System on Mobile
Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409
Marzanah A. Jabar, Niloofar Yousefi, Ramin Ahmadi,
Mohammad Yaser Shafazand, Fatimah Sidi
An Enhanced Parameter-Free Subsequence Time Series Clustering for
High-Variability-Width Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419
Navin Madicar, Haemwaan Sivaraks, Sura Rodpongpun,
Chotirat Ann Ratanamahatana


Contents

XV

An Optimized Classification Approach Based on Genetic Algorithms
Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431

Ines Bouzouita
Comparative Performance Analysis of Negative Selection Algorithm with
Immune and Classification Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441
Ayodele Lasisi, Rozaida Ghazali, Tutut Herawan
Content Based Image Retrieval Using MPEG-7 and Histogram . . . . . . . . . . . 453
Muhammad Imran, Rathiah Hashim, Noor Elaiza Abd Khalid
Cost-Sensitive Bayesian Network Learning Using
Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467
Eman Nashnush, Sunil Vadera
Data Treatment Effects on Classification Accuracies of Bipedal Running
and Walking Motions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477
Wei Ping Loh, Choo Wooi H’ng
Experimental Analysis of Firefly Algorithms for Divisive Clustering of
Web Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487
Athraa Jasim Mohammed, Yuhanis Yusof, Husniza Husni
Extended Naïve Bayes for Group Based Classification . . . . . . . . . . . . . . . . . . . 497
Noor Azah Samsudin, Andrew P. Bradley
Improvement of Audio Feature Extraction Techniques in Traditional
Indian Musical Instrument . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507
Kohshelan, Noorhaniza Wahid
Increasing Failure Recovery Probability of Tourism-Related Web
Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517
Hadi Saboohi, Amineh Amini, Tutut Herawan
Mining Critical Least Association Rule from Oral Cancer Dataset . . . . . . . . 529
Zailani Abdullah, Fatiha Mohd, Md Yazid Mohd Saman,
Mustafa Mat Deris, Tutut Herawan, Abd Razak Hamdan
Music Emotion Classification (MEC): Exploiting Vocal and Instrumental
Sound Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539
Mudiana Mokhsin Misron, Nurlaila Rosli, Norehan Abdul Manaf,
Hamizan Abdul Halim

Resolving Uncertainty Information Using Case-Based Reasoning
Approach in Weight-Loss Participatory Sensing Campaign . . . . . . . . . . . . . . 551
Andita Suci Pratiwi, Syarulnaziah Anawar


XVI

Contents

Towards a Model-Based Framework for Integrating Usability Evaluation
Techniques in Agile Software Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561
Saad Masood Butt, Azura Onn, Moaz Masood Butt,
Nadra Tabassam

Workshop on Nature Inspired Computing and Its
Applications
Emulating Pencil Sketches from 2D Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571
Azhan Ahmad, Somnuk Phon-Amnuaisuk, Peter D. Shannon
Router Redundancy with Enhanced VRRP for Intelligent Message
Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581
Haja Mohd Saleem, Mohd Fadzil Hassan, Seyed M. Buhari
Selecting Most Suitable Members for Neural Network Ensemble Rainfall
Forecasting Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591
Harshani Nagahamulla, Uditha Ratnayake, Asanga Ratnaweera
Simulating Basic Cell Processes with an Artificial Chemistry System . . . . . . 603
Chien-Le Goh, Hong Tat Ewe, Yong Kheng Goh
The Effectiveness of Sampling Methods for the Imbalanced Network
Intrusion Detection Data Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613
Kok-Chin Khor, Choo-Yee Ting, Somnuk Phon-Amnuaisuk

Workshop on Machine Learning for Big Data Computing
A Clustering Based Technique for Large Scale Prioritization during
Requirements Elicitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623
Philip Achimugu, Ali Selamat, Roliana Ibrahim
A Comparative Evaluation of State-of-the-Art Cloud Migration
Optimization Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633
Abdelzahir Abdelmaboud, Dayang N.A. Jawawi, Imran Ghani,
Abubakar Elsafi
A Review of Intelligent Methods for Pre-fetching in Cloud Computing
Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647
Nur Syahela Hussien, Sarina Sulaiman, Siti Mariyam Shamsuddin
Enhanced Rules Application Order Approach to Stem Reduplication
Words in Malay Texts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657
M.N. Kassim, Mohd Aizaini Maarof, Anazida Zainal
Islamic Web Content Filtering and Categorization on Deviant Teaching . . . . 667
Nurfazrina Mohd Zamry, Mohd Aizaini Maarof, Anazida Zainal


Contents

XVII

Multiobjective Differential Evolutionary Neural Network for Multi Class
Pattern Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 679
Ashraf Osman Ibrahim, Siti Mariyam Shamsuddin,
Sultan Noman Qasem
Ontology Development to Handle Semantic Relationship between Moodle
E-Learning and Question Bank System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 691
Arda Yunianta, Norazah Yusof, Herlina Jayadianti,
Mohd Shahizan Othman, Shaffika Suhaimi

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703


A Fuzzy Time Series Model in Road Accidents Forecast
Lazim Abdullah and Chye Ling Gan
School of Informatics and Applied Mathematics,
Universiti Malaysia Terengganu,
21030 Kuala Terengganu, Malaysia
{lazim abdullah,lazim_m}@umt.edu.my

Abstract. Many researchers have explored fuzzy time series forecasting models
with the purpose to improve accuracy. Recently, Liu et al., have proposed a new
method, which an improved version of Hwang et al., method. The method has
proposed several properties to improve the accuracy of forecast such as levels
of window base, length of interval, degrees of membership values, and
existence of outliers. Despite these improvements, far too little attention has
been paid to real data applications. Based on these advantageous, this paper
investigates the feasibility and performance of Liu et al., model to Malaysian
road accidents data. Twenty eight years of road accidents data is employed as
experimental datasets. The computational results of the model show that the
performance measure of mean absolute forecasting error is less than 10 percent.
Thus it would be suggested that the Liu et al., model practically fit with the
Malaysian road accidents data.
Keywords: Fuzzy time series, time-variant forecast, length of interval, window
base, road accidents.

1

Introduction

In the new global economy, forecasting plays important activities in daily lives as it
often has been used to forecast weather, agriculture produce, stock price and students’
enrolment. One of the most traditional approaches in forecasting is Box Jenkins
model which was proposed by Box and Jenkin [1]. Traditional forecasting methods
can be dealt with many forecasting cases. However, one of the limitations in
implementing traditional forecasting is its incapability in fulfilling sufficient historical
data. To solve this problem, Song and Chissom [2] proposed the concept of fuzzy
time series. This concept was proposed when they attempted to implement fuzzy set
theory for forecasting task. Over time, this method had been well received by
researchers due to its capability in dealing with vague and incomplete data. Fuzzy set
theory is created for handling of uncertain environment and fuzzy numbers provided
various opportunities to compile difficult and complex problems. Fuzzy time series
method is appeared successfully dealt with uncertainty of series and in some
empirical studies, it provides higher accuracy. Some of the recent research in fuzzy
time series performances can be retrieved from [3], [4], [5]. However, the issues of
accuracy and performance of fuzzy time series still very much debated.
T. Herawan et al. (eds.), Recent Advances on Soft Computing and Data Mining
SCDM 2014, Advances in Intelligent Systems and Computing 287,
DOI: 10.1007/978-3-319-07692-8_1, © Springer International Publishing Switzerland 2014

1


2

L. Abdullah and C.L. Gan

Defining window bases variable w is one the efforts to increase forecasting
accuracy especially in time-variant fuzzy time series. Song and Chissom [6] have
showed that the effect of forecasting result with the changes of w. Hwang et al., [7]

used Song and Chissom’s method as a basis to calculate the variations of the
historical data and recognition of window base, but one level is extended to w level.
Hwang et al., [7] forecasted results were better than those presented by Song and
Chissom’s method due to the fact that the proposed method simplifies the arithmetic
operation process. However, the time-variant model of Hwang et al.,[7] was not
withstand as time keeps moving on and on. Liu et al., [8] took an initiative to revise
Hwang et al.’s model with the purpose to overcome several drawbacks. Among the
drawbacks of Hwang et al., are the length of intervals and number of intervals.
Huarng [9] and Huarng and Yu [10] argue that different lengths and numbers of
intervals may affect the accuracy of forecast. Furthermore, Hwang et al., method did
not provide suggestions with regard to the determination of window base. Also, they
uniformly set 0.5 as the membership values in the fuzzy set, without giving variations
in degree. With a good intention to improve these drawbacks, Liu et al., [8] proposed
a new model with the goals to effectively determine the best interval length, level of
window base and degrees of membership values. These moves surely targeted to
increase the accuracy of forecasted values. Although the Liu et al.,’s method is
considered as a excellent technique to increase accuracy but the method has never
been conceptualized in real applications. So far, however, there have been little
discussions about testing this improved fuzzy time series to road accidents data.
In road accident forecast, many researchers used traditional ARMA to correct the
error terms. Gandhi and Hu [11], for example, used a differential equation model to
represent the accident mechanism with time-varying parameters and an ARMA
process of white noise is attached to model the equation error. Another example, is
the combination of a regression model and ARIMA model presented by Van den
Bossche et al., [12]. In Malaysia, Law et al., [13] made a projection of the vehicle
ownership rate to the year 2010 and use this projection to predict the road accident
death in 2010 by using an ARIMA model. The projection takes into account the
changes in population and the vehicle ownership rate. The relationship between death
rate and population, vehicle ownership rate were described utilizing transfer noise
function in ARIMA analysis. Other than ARIMA, Chang [14] analyzed freeway

accident frequencies using negative binomial regression versus artificial neural
network. In line with well acceptance of fuzzy knowledge in forecasting research,
Jilani and Burney [15] recently presented a new multivariate stochastic fuzzy
forecasting model. These new methods were applied for forecasting total number of
car road accidents casualties in Belgium using four secondary factors. However there
have been no studies to bring the light of the relationship between fuzzy time series
model and road accidents data. The recent discovery of an improved fuzzy time series
motivates the need to explore the applicability of the time variant fuzzy time series to
road accidents data. The present paper takes an initiative to implement the fuzzy time
series in forecasting of Malaysian road accidents. Specifically this paper intends to
test the variant time fuzzy time series Liu et al., [8] model to the Malaysian road
accidents data.


A Fuzzy Time Series Model in Road Accidents Forecast

3

This paper is organized as follows. Conceptual definitions of time-variant fuzzy
time series, window bases and its affiliates are discussed in Section 2. The vigour of
computational steps of road accidents data are elucidated in Section 3. The short
conclusion finally presented in Section 4.

2

Preliminaries

The concept of fuzzy logic and fuzzy set theory were introduced to cope with the
ambiguity and uncertainty of most of the real-world problems. Chissom [6]
introduced the concept of fuzzy time series and since then a number of variants were

published by many authors. The basic concepts of fuzzy set theory and fuzzy time
series are given by Song and Chissom [2] and some of the essentials are reproduced to
make the study self-contained. The basic concepts of fuzzy time series are explained
by Definition 1 to Definition 4.
Definition 1. Y(t)(t =...,0,1,2,...), is a subset of R. Let Y(t) be the universe of
discourse defined by the fuzzy set μ i (t ) .If F(t) consists of μ i (t ) (i =1,2,...), F(t) is
called a fuzzy time series on Y(t), i=1,2,…
Definition 2. If there exists a fuzzy relationship R(t-1, t), such that F(t) =F(t-1) ◦R(t-1,
t), where ◦ is an arithmetic operator, then F(t) is said to be caused by F(t-1). The
relationship between F(t) and F(t-1) can be denoted by F(t-1) →F(t).
Definition 3. Suppose F(t) is calculated by F(t-1) only, and F(t) = F(t-1) ◦R(t-1, t). For
any t, if R(t-1, t) is independent of t, then F(t) is considered a time-invariant fuzzy
time series. Otherwise, F(t) is time-variant.
Definition 4. Suppose F(t-1) = Ãi and F(t) = Ãj , a fuzzy logical relationship can be
defined as Ãi→Ãj where Ãi and Ãj are called the left-hand side and right-hand side of
the fuzzy logical relationship, respectively.
These definitions become the basis in explaining fuzzy time series Liu et al., method.
Liu et al., proposed the forecasting method with the aim at improving Hwang et al.’s
method. Liu et al., method’s has successfully overcome some drawbacks of Hwang et
al., method’s by finding the best combination between the length of intervals and the
window bases. Detailed algorithms of Liu et al. [8], are not explained in this paper.

3

Implementation

In this experiment, Liu et al.,’s method is tested to the Malaysian road accidents
data. An official road accidents data released by Royal Malaysian Police [16] are
employed to the model. The calculation is executed in accordance with the proposed
method. For the purpose of clarity and simplicity, the following computations are

4

L. Abdullah and C.L. Gan

limited to forecasted value of the year 2009. Also, due to space limitation, the
historical data from the year 2004 to 2008 are accounted in these computational steps.
Step 1: Collect the historical data of road accident in Malaysia., Dvt, for the year
2004 to 2008.
Step 2: Examine outliers. The studentized residual analysis method is applied to
determine whether there exist outliers in historical data. Statistical software is used to
calculate the residual. Table 1 shows the outliers examination for the last five years
before 2009.
Table 1. Studentized deleted residual of the historical data
Year

Number of Road Accident, Dvt

Studentized deleted residual

2004

326,815

1.0272

2005

328,264

0.60248

2006

341,252

0.62921

2007

363,319

1.01975

2008

373,047

0.91899

It shows that all of studentized deleted residuals| are less than 2.5, thus confirm that
there are no outliers in the historical data.
Step 3: Calculate the variation of the historical data. For example, the variation of
year 2005 is calculated as follow:
Variation= Rv2005-Rv2004= 328,264-326,815= 1,449
Similarly, the variations of all data are computed. It can be seen that the minimum
of the variations in the data is -4,595 (Dmin) and the maximum is 28,162 (Dmax). To
simplify computations, let D1=405 and D2=338.
U=[Dmin -D1 , Dmax + D2] =[ -4,595-405, 28,162+338]=[-5,000, 28,500]

Step 4: Calculate Ad by dividing all the variations (Step 3) with number of data
minus one:

 10,904 + 5054... + 9,728   33,6663 
Ad = int
=
 = 12,023.68 ≈ 12,200
29 − 1

  28 
For simplicity, Ad=12,200 is divided by 10 that yields the unit value 1,220. Thus,
there are 10 possible interval lengths (1,220, 2,440, …, 12,200). The membership
function for l=1,220 is 0.9. When l=2,440, its membership value is 0.8. The rest of
membership function can be obtained in similar fashion. The corresponding relations
are shown in Table 2.


A Fuzzy Time Series Model in Road Accidents Forecast

5

Table 2. Interval length and the corresponding membership values for the road accident
problem
Interval

Interval length

Membership value

1

1,220

0.9

2

2,440

0.8

3

3,660

0.7

4

4,880

0.6

5

6,100

0.5

6

7,320

0.4

7

8,540

0.3

8

9,760

0.2

9

10,980

0.1

10

12,200

0

Step 5: Using l=3,660 or membership value=0.7 as an example, the number of

intervals (fuzzy set) is calculated as follows:
Number of interval =

29,000 − (−5,000)
= 9.2896 ≈ 10
3,660

Therefore, there are 10 intervals (fuzzy sets) and the interval midpoints are shown
below.
u1= [-5,000, -1340],
u2= [-1,340, 2,320],
u3= [2,320, 5,980],
u4= [5,980, 9,640],
u5= [9,640, 13,300],
u6= [13,300, 16,960],
u7= [16,960, 20,620],
u8= [20,620, 24,280],
u9= [24,280, 27,940],
u10= [27,940, 31,600],

Midpoint =-3,170
Midpoint = 490
Midpoint =4,150
Midpoint =7,810
Midpoint =11,470
Midpoint =15,130
Midpoint =18,790
Midpoint =22,450
Midpoint =26,110
Midpoint =29,770

As shown in Table 2, when l=3,660, its membership value is 0.7. Thus, the fuzzy
sets can be defined as follows.
A1=1/u1+0.7/u2+0/u3+0/u4+0/u5+0/u6+0/u7+0/u8+0/u9+0/u10
A2=0.7/u1+1/u2+0.7/u3+0/u4+0/u5+0/u6+0/u7+0/u8+0/u9+0/u10
A3=0/u1+0.7/u2+1/u3+0.7/u4+0/u5+0/u6+0/u7+0/u8+0/u9+0/u10
A4=0/u1+0/u2+0.7/u3+1/u4+0.7/u5+0/u6+0/u7+0/u8+0/u9+0/u10
A5=0/u1+0/u2+0/u3+0.7/u4+1/u5+0.7/u6+0/u7+0/u8+0/u9+0/u10
A6=0/u1+0/u2+0/u3+0/u4+0.7/u5+1/u6+0.7/u7+0/u8+0/u9+0/u10


6

L. Abdullah and C.L. Gan

A7=0/u1+0/u2+0/u3+0/u4+0/u5+0.7/u6+1/u7+0.7/u8+0/u9+0/u10
A8=0/u1+0/u2+0/u3+0/u4+0/u5+0/u6+0.7/u7+1/u8+0.7/u9+0/u10
A9=0/u1+0/u2+0/u3+0/u4+0/u5+0/u6+0/u7+0.7/u8+1/u9+0.7/u10
A10=0/u1+0/u2+0/u3+0/u4+0/u5+0/u6+0/u7+0/u8+0.7/u9+1/u10
Step 6: Fuzzify the variation of the data. If the variation at time i is within the
scope of uj, then it belongs to fuzzy set Ãj. The fuzzy variation at time i is denoted as
F (i). The variation between year 1991 and 1992 is 22,041, which falls in the range of
u8= [20,620, 24,280], so it belongs to the fuzzy set Ã8. That is F(1992)= Ã8. Similarly,
the corresponding fuzzy sets of the remaining variations can be obtained.
Step 7: Calculate the fuzzy time series F(t) at window base w. The window base
has to be more than or equal to 2 in order to perform a fuzzy composition operation.
Therefore, w is set as 2 initially. Let C(t) be the criterion matrix of F(t) and OW(t) be
the operation matrix at window base w.
The fuzzy relation matrix R(t) is computed by performing the fuzzy composition
operation of C(t) and OW(t).

To get F(t), we can calculate the maximum of every column in matrix R(t).
Assume the window base is 4 and l=3,660. For example, the criterion matrix
C(2009) of F(2009) is F(2008).

C (2009) = F (2008) = [ A5 ] = [0 0 0 0.7 1 0.7 0 0 0 0]
~

Meanwhile, the composition matrix is O4(2009) is composed of F(2007), F(2006)
and F(2005).

 F (2007)
O =  F (2006)
 F (2005) 
4

~ 
 A8   0 0 0
0 0 0 0.7 1 0.7 0
~  
=  A5  =  0 0 0 0.7 1 0.7 0 0 0 0
 ~  0.7 1 0.7 0 0 0
0 0 0 0
 A2  
 

Apply the fuzzy composition operation to compute R(2009).

R(2009) = O 4 (2009) ⊗ C (2009)
0 × 0 .7 0 × 1 0 × 0 .7 0 .7 × 0 1 × 0 0 .7 × 0 0 × 0 
 0×0 0×0 0×0

=  0 × 0 0 × 0 0 × 0 0.7 × 0.7 1 × 1 0.7 × 0.7 0 × 0 0 × 0 0 × 0 0 × 0
0.7 × 0 1 × 0 0.7 × 0 0 × 0.7 0 × 1 0 × 0.7
0 × 0 0 × 0 0 × 0 0 × 0

Find the maximum at each column of R(2009) and F(2009) can be obtained as

F(2009)= [0 0 0 0.49 1 0.49 0 0 0 0]
Part of the results for F is given in Table 3


A Fuzzy Time Series Model in Road Accidents Forecast

7

Table 3. Part of the fuzzy time series at window base w= 4
F
Year

Fuzzy
Variation Variation

u1

u2

u3

u4

u5

u6

u7

u8

u9

u10

2004

28,162

Ã10

0

0

0

0

0

1.4

1.4

0.49

0

0

2005

1,449

Ã2

0

0

0

0

0

0

0

0

0

0

2006

12,988

Ã5

0

0

0

0

0

0

0

0

0

0

2007

22,067

Ã8

0

0

0

0

0

0.49

0

0

0

0

2008

9,728

Ã5

0

0

0

0

0

0

0

0

0.49

0

0

0

0

0.49

1

0.49

0

0

0

0

2009

Step 8: In F(2009), there are three nonzero intervals: u4, u5, and u6, while their
interval midpoint are 7,810, 11,470, 15,130, individually. The computation of Cv2009
is as follows:

 0.49 × 7,810 + 1 × 11,470 + 0.49 × 15,130 
Cv2009 = 
 = 7,570
3


Next, calculate the forecasted value of Fv2009m,
Fv2009=Cv2009+Rv2008=7,570+373,047 =380,617
Step 9: To obtain the best forecasted values, the search algorithm is use to identify
the best window base and interval length. Step 5 to step 8 are repeated for different
window base and interval length. The best forecasted value is computed for different
window base and interval length. Mean absolute deviation, MAD of each window
base and interval length are calculated using the formula.


MAD =

n

t −1

| Fvt − Rvt |

n − w −1

MAD values for each different window base and interval length are presented in
Table 4.