NDLI: Improved Binary Sailfish Optimizer Based on Adaptive <italic>β-Hill Climbing for Feature Selection

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Zong Woo Geem Ram Sarkar Kushal Kanti Ghosh Shameem Ahmed Pawan Kumar Singh
Abstract	Feature selection (FS), an important pre-processing step in the fields of machine learning and data mining, has immense impact on the outcome of the corresponding learning models. Basically, it aims to remove all possible irrelevant as well as redundant features from a feature vector, thereby enhancing the performance of the overall prediction or classification model. Over the years, meta-heuristic optimization techniques have been applied for FS, as these are able to overcome the limitations of traditional optimization approaches. In this work, we introduce a binary variant of the recently-proposed Sailfish Optimizer (SFO), named as Binary Sailfish (BSF) optimizer, to solve FS problems. Sigmoid transfer function is utilized here to map the continuous search space of SFO to a binary one. In order to improve the exploitation ability of the BSF optimizer, we amalgamate another recently proposed meta-heuristic algorithm, namely adaptive β-hill climbing (AβHC) with BSF optimizer. The proposed BSF and AβBSF algorithms are applied on 18 standard UCI datasets and compared with 10 state-of-the-art meta-heuristic FS methods. The results demonstrate the superiority of both BSF and AβBSF algorithms in solving FS problems. The source code of this work is available in https://github.com/Rangerix/MetaheuristicOptimization.
e-ISSN	21693536
DOI	10.1109/ACCESS.2020.2991543
Journal	IEEE Access
Volume Number	8
Language	English
Publisher	IEEE
Publisher Date	2020-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Electrical Engineering. Electronics. Nuclear Engineering Hybrid Optimization Uci Dataset Binary Sailfish Optimizer Feature Selection
Content Type	Text
Resource Type	Article

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