A multi-objective Artificial Bee Colony algorithm for cost-sensitive subset selection.

Feature selection typically aims to select a feature subset that maximally contributes to the performance of a further process (such as clustering, classification and regression). Most of the current feature selection methods handle all features in the dataset with the same importance while evaluating the possible feature subsets in a solution space. However, this case may not be appropriate since each feature in a dataset comes with its own impact and importance. In particular, each feature may provide a different cost to achieve some specific purposes. To address this issue, we introduce an improved multi-objective artificial bee colony-based cost-sensitive subset selection method which simultaneously tries to minimize two main conflicting objectives: the classification error rate and the feature cost. According to the results on well-known benchmarks, the proposed cost-sensitive subset selection approach outperforms the recently introduced multi-objective variants of the artificial bee colony, particle swarm optimization and genetic algorithms. To the best of our knowledge, this work is one of the earliest studies on multi-objective cost-sensitive subset selection in the literature. [ABSTRACT FROM AUTHOR]