Title | ||
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Spatial bound whale optimization algorithm: an efficient high-dimensional feature selection approach |
Abstract | ||
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Selecting a subset of candidate features is one of the important steps in the data mining process. The ultimate goal of feature selection is to select an optimal number of high-quality features that can maximize the performance of the learning algorithm. However, this problem becomes challenging when the number of features increases in a dataset. Hence, advanced optimization techniques are used these days to search for the optimal feature combinations. Whale Optimization Algorithm (WOA) is a recent metaheuristic that has successfully applied to different optimization problems. In this work, we propose a new variant of WOA (SBWOA) based on spatial bounding strategy to play the role of finding the potential features from the high-dimensional feature space. Also, a simplified version of SBWOA is introduced in an attempt to maintain a low computational complexity. The effectiveness of the proposed approach was validated on 16 high-dimensional datasets gathered from Arizona State University, and the results are compared with the other eight state-of-the-art feature selection methods. Among the competitors, SBWOA has achieved the highest accuracy for most datasets such as TOX_171, Colon, and Prostate_GE. The results obtained demonstrate the supremacy of the proposed approaches over the comparison methods. |
Year | DOI | Venue |
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2021 | 10.1007/s00521-021-06224-y | NEURAL COMPUTING & APPLICATIONS |
Keywords | DocType | Volume |
Whale optimization algorithm, Feature selection, Data mining, Classification, High Dimensional Data, Optimization, Benchmark, WOA, Swarm intelligence, Evolutionary | Journal | 33 |
Issue | ISSN | Citations |
23 | 0941-0643 | 0 |
PageRank | References | Authors |
0.34 | 50 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Jingwei Too | 1 | 3 | 0.70 |
Majdi Mafarja | 2 | 574 | 20.00 |
Seyedali Mirjalili | 3 | 3949 | 140.80 |