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