Abstract
Feature selection represents a fundamental preprocessing phase in machine learning as well as data mining applications. It reduces the dimensionality of feature space by dismissing irrelevant and redundant features, which leads to better classification accuracy and less computational cost. This paper presents a new wrapper feature subset selection model based on a recently designed optimisation technique called migrating birds optimisation (MBO). Initialisation issue regarding MBO is explored to study its implications on the model behaviour by experimenting different initialisation strategies. A neighbourhood based on information gain was designed to improve the search effectiveness. The performance of the proposed model named MBO-FS is compared with some state-of-the-art methods regarding the task of feature selection on 11 UCI datasets. Simulation results show that MBO-FS method achieves promising classification accuracy using a smaller feature set.
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