Attribute Selection Model For Optimal Local Search And Global Search

Abstract

Attribute selection also known as feature selection is an essential process in data sets that comprise numerous numbers of input attributes. However, finding the optimal combination of algorithms for producing a good set of attributes has remained a challenging task. The aim of this paper is to find a list of an optimal combination search methods and reduction algorithm for attribute selection. The research process involves 2 phases: finding a list of an optimal combination search methods and reduction algorithm. The combination is known as model. Results are in terms of percentage of accuracy and number of selected attributes. Six (6) datasets were used for experiment. The final output is a list of optimal combination search methods and reduction algorithm. The experimental results conducted on public real dataset reveals that the model consistently shows the suitability to perform good classification task on the selected dataset. Significant improvement in accuracy and optimal number of attribute selection is achieved with a list of combination algorithms used.