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Feature selection is an important preprocessing step for classificationproblems. It deals with selecting near optimal features in the originaldataset. Feature selection is an NP-hard problem, so meta-heuristics can bemore efficient than exact methods. In this work, Ant Lion Optimizer (ALO),which is a recent metaheuristic algorithm, is employed as a wrapper featureselection method. Six variants of ALO are proposed where each employ a transferfunction to map a continuous search space to a discrete search space. Theperformance of the proposed approaches is tested on eighteen UCI datasets andcompared to a number of existing approaches in the literature: Particle SwarmOptimization, Gravitational Search Algorithm, and two existing ALO-basedapproaches. Computational experiments show that the proposed approachesefficiently explore the feature space and select the most informative features,which help to improve the classification accuracy.

S-Shaped vs. V-Shaped Transfer Functions for Antlion Optimization Algorithm in Feature Selection Problems