  Feature selection is an important preprocessing step for classification
problems. It deals with selecting near optimal features in the original
dataset. Feature selection is an NP-hard problem, so meta-heuristics can be
more efficient than exact methods. In this work, Ant Lion Optimizer (ALO),
which is a recent metaheuristic algorithm, is employed as a wrapper feature
selection method. Six variants of ALO are proposed where each employ a transfer
function to map a continuous search space to a discrete search space. The
performance of the proposed approaches is tested on eighteen UCI datasets and
compared to a number of existing approaches in the literature: Particle Swarm
Optimization, Gravitational Search Algorithm, and two existing ALO-based
approaches. Computational experiments show that the proposed approaches
efficiently explore the feature space and select the most informative features,
which help to improve the classification accuracy.
