  A supervised feature selection method selects an appropriate but concise set
of features to differentiate classes, which is highly expensive for large-scale
datasets. Therefore, feature selection should aim at both minimizing the number
of selected features and maximizing the accuracy of classification, or any
other task. However, this crucial task is computationally highly demanding on
many real-world datasets and requires a very efficient algorithm to reach a set
of optimal features with a limited number of fitness evaluations. For this
purpose, we have proposed the binary multi-objective coordinate search (MOCS)
algorithm to solve large-scale feature selection problems. To the best of our
knowledge, the proposed algorithm in this paper is the first multi-objective
coordinate search algorithm. In this method, we generate new individuals by
flipping a variable of the candidate solutions on the Pareto front. This
enables us to investigate the effectiveness of each feature in the
corresponding subset. In fact, this strategy can play the role of crossover and
mutation operators to generate distinct subsets of features. The reported
results indicate the significant superiority of our method over NSGA-II, on
five real-world large-scale datasets, particularly when the computing budget is
limited. Moreover, this simple hyper-parameter-free algorithm can solve feature
selection much faster and more efficiently than NSGA-II.
