An Excited Binary Grey Wolf Optimizer for Feature Selection in Highly Dimensional Datasets

Davies Segera, Mwangi Mbuthia, Abraham Nyete


Currently, feature selection is an important but challenging task in both data mining and machine learning, especially when handling highly dimensioned datasets with noisy, redundant and irrelevant attributes. These datasets are characterized by many attributes with limited sample-sizes, making classification models overfit. Thus, there is a dire need to develop efficient feature selection techniques to aid in deriving an optimal informative subset of features from these datasets prior to classification. Although grey wolf optimizer (GWO) has been widely utilized in feature selection with promising results, it is normally trapped in the local optimum resulting into semi-optimal solutions. This is because its position-updated equation is good at exploitation but poor at exploration. In this paper, we propose an improved algorithm called excited binary grey wolf optimizer (EBGWO). In order to improve on exploration, a new position-updating criterion is adopted by utilizing the fitness values of vectors 𝑋⃗ଵ, 𝑋⃗ଶ and 𝑋⃗ଷ to determine new candidate individuals. Moreover, in order to make full use of and balance the exploration and exploitation of the existing BGWO, a novel nonlinear control parameter strategy is introduced, i.e. the control parameter of 𝑎⃗ is innovatively decreased via the concept of the complete current response of a direct current (DC) excited resistor-capacitor (RC) circuit. The experimental results on seven standard gene expression datasets demonstrate the appropriateness and efficiency of the fitness value based position-updating criterion and the novel nonlinear control strategy in feature selection. Moreover, EBGWO achieved a more compact set of features along with the highest accuracy among all the contenders considered in this paper.


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