Paper Info
Title
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 fit 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-the-art methods in terms of both classification accuracy and number of features selected. The source code of this work is available in https://github.com/Rangerix/MetaheuristicOptimization.
Year
DOI
Venue
2021
10.1007/s00521-020-05560-9
NEURAL COMPUTING & APPLICATIONS
Keywords
DocType
Volume
Manta ray foraging optimization, Feature selection, Optimization, Classification, Transfer functions, Metaheuristic
Journal
33
Issue
ISSN
Citations 
17
0941-0643
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Kushal Kanti Ghosh1122.93
Ritam Guha273.47
Suman Kumar Bera321.79
Neeraj Kumar42889236.13
Ram Sarkar542068.85