S-Shaped versus V-Shaped Transfer Functions for Binary Manta Ray Foraging Optimization in Feature Selection Problem

Abstract Feature selection (FS) is considered as one of the core concepts in the areas of machine learning and data mining which immensely impacts the performance of classification model. Through FS, irrelevant or partially relevant features can be eliminated which in turn helps in enhancing the performance of the model. Over the years, researchers have applied different meta-heuristic optimization techniques for the purpose of FS as these overcome the limitations of traditional optimization approaches. Going by the trend, we introduce a new FS approach based on a recently proposed meta-heuristic algorithm called Manta Ray Foraging Optimization (MRFO) which is developed following the food foraging nature of the Manta rays, one of the largest known marine creatures. As MRFO is apposite for continuous search space problems, we have adapted a binary version of MRFO to t it into the problem of FS by applying eight different transfer functions belonging to two different families: S-shaped and V-shaped. We have evaluated the eight binary versions of MRFO on 18 standard UCI datasets. Of these, the best one is considered for comparison with 16 recently proposed meta-heuristic FS approaches. The results show that MRFO outperforms the state-of-art methods in terms of both classification accuracy and number of features selected.