Open AccessOPTIMIZATION OF ATTRIBUTE SELECTION MODEL USING BIO-INSPIRED ALGORITHMS
Author(s) -
Mohammad Aizat Basir,
Yuhanis Yusof,
Mohamed Saifullah Hussin
Publication year - 2018
Publication title -
journal of ict
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.217
H-Index - 10
eISSN - 2180-3862
pISSN - 1675-414X
DOI - 10.32890/jict2019.18.1.8280
Subject(s) - feature selection , computer science , selection (genetic algorithm) , data mining , consistency (knowledge bases) , set (abstract data type) , process (computing) , artificial intelligence , feature (linguistics) , data set , machine learning , algorithm , linguistics , philosophy , programming language , operating system
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.