Sunday, October 21, 2018
Text Size

Forthcoming Articles

These articles have been peer-reviewed and accepted for publication in JICT, but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the JICT standard. Additionally, titles, authors, abstracts and keywords may change before publication.

1Mohammad Aizat Basir, 2Yuhanis Yusof & 1Mohamed Saifullah Hussin
1School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu,Malaysia
2School of Computing, Universiti Utara Malaysia, Malaysia
Attribute selection which is also known as feature selection is an essential process that is relevant to predictive analysis. To date, various feature selection algorithms have been introduced, nevertheless they all work independently. Hence, reducing the consistency of the accuracy rate. The aim of this paper is to investigate the use of bio-inspired search algorithms in producing optimal attribute set. This is achieved in two stages; 1) create attribute selection models by combining search method and feature selection algorithms, and 2) determine an optimized attribute set by employing bio-inspired algorithms. Classification performance of the produced attribute set is analyzed based on accuracy and number of selected attributes. Experimental results conducted on six (6) public real datasets reveal that the feature selection model with the implementation of bio-inspired search algorithm consistently performs good classification (i.e higher accuracy with fewer numbers of attributes) on the selected data set. Such a finding indicates that bio-inspired algorithms can contribute in identifying the few most important features to be used in data mining model construction.
Keywords: Feature selection, bio-inspired algorithms, data classification, data mining.

Shahliza Abd Halim, Dayang Norhayati Abang Jawawi & Muhammad Sahak
School of Computing, Faculty of Engineering
Universiti Teknologi Malaysia, Malaysia
To achieve the goal of creating products for a specific market segment, implementation of Software Product Line (SPL) is required to fulfill specific needs of customers by managing a set of common features and exploiting the variabilities between the products. Testing product-by-product is not feasible in SPL due to the combinatorial explosion of product number, thus, Test Case Prioritization (TCP) is needed to select a few test cases which could yield high number of faults. Among the most promising TCP techniques is similarity-based TCP technique which consists of similarity distance measure and prioritization algorithm. The goal of this paper is to propose an enhanced string distance and prioritization algorithm which could reorder the test cases resulting to higher rate of fault detection. Comparative study has been done between different string distance measures and prioritization algorithms to select the best techniques for similarity-based test case prioritization. Identified enhancements have been implemented to both techniques for a better adoption of prioritizing SPL test cases. Experiment has been done in order to identify the effectiveness of enhancements done for combination of both techniques. Result shows the effectiveness of the combination where it achieved highest average fault detection rate, attained fastest execution time for highest number of test cases and accomplished 41.25% average rate of fault detection. The result proves that the combination of both techniques improve SPL testing effectiveness compared to other existing techniques.
Keywords: Combinatorial interaction testing, similarity distance, string based prioritization, feature model, sampling algorithm.

Universiti Utara Malaysia Press
 Universiti Utara Malaysia, 06010 UUM Sintok
Kedah Darul Aman, MALAYSIA
Phone: +604-928 4816, Fax : +604-928 4792

All Right Reserved. Copyright © 2010, Universiti Utara Malaysia Press