Improved Binary Sailfish Optimizer Based on Adaptiveβ-Hill Climbing for Feature Selection

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. INDEX TERMS Binary sailfish optimizer, feature selection, adaptive β-hill climbing, hybrid optimization, UCI dataset.

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

“…Here, (1 -HMCR) is considered as the probability of using range of available values, which improves the exploration capability of proposed algorithm. In each iteration, the EH interacts with each other following the equation (10), and the improvement is assessed by its fitness function as defined in equation (20). The EH value is equivalent to the position analogy in III-B.…”

“…So, the problem becomes changing size of the subset efficiently. Some of the wrapper based FS methods are binary partcile swarm optimization using SVM [8], FS using ant colony optimization (ACO) [9], adaptive β binary sailfish optimizer (AβBSF) [10], FS using hybrid of grey wolf optimizer(GWO) and whale optimization algorithm (WOA) [11] etc. • Embedded: This method is based on the combination of filter and wrapper methods.…”

bSSA: Binary Salp Swarm Algorithm With Hybrid Data Transformation for Feature Selection

“…The proposed hybrid FS method shows better results over high-dimensional datasets but it shows unstable results on high-dimensional biomedical datasets. Ghosh et al [81] used enhanced binary sailfish optimizer and β-hill climbing for finding relevant features from the dataset. Alweshah et al [82] presented a hybrid mine blast FS method to enhance the quality of selected features.…”

Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer

“…Recently, meta-heuristic optimization algorithms [21] have become popular amongst researchers. These algorithms have proved their superiority in different fields: medicine, data mining, pattern recognition, finance [22,23,24] etc. Many such optimization algorithms have been used for image en-hancement and established their superiority over the traditional approaches like HE.…”