Multiple strategies based Grey Wolf Optimizer for feature selection in performance evaluation of open-ended funds

Chang, Dan; Rao, Congjun; Xiao, Xinping; Hu, Fuyan; Goh, Mark

doi:10.1016/j.swevo.2024.101518

Cited by 9 publications

(2 citation statements)

References 48 publications

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“…For instance, the work [119] developed a K-means transition algorithm to enhance CS and BHO algorithms, exhibiting competitive performance in solving the set covering problem. Additionally, the work [120] proposed a multi-stage GWO approach to enhance feature selection for fund performance evaluation, overcoming local optima issues and reducing feature dimensions and classification error rates. Furthermore, the work [121] introduced a PSO-based feature selection method with explicit representation and feature grouping, addressing memory and computational challenges in PSO.…”

Section: Optimization Problemmentioning

confidence: 99%

Investigating the Performance of a Novel Modified Binary Black Hole Optimization Algorithm for Enhancing Feature Selection

Al-Eiadeh,

Qaddoura,

Abdallah

2024

Preprint

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High dimensional datasets are highly likely to have redundant, irrelevant, and noisy features that negatively affect the performance of the classification algorithms. Selecting the most relevant features and reducing the dimensions of datasets by removing the undesired features is a dimensional reduction technique called Feature Selection (FS). In this paper, we propose an FS approach based on the Black Hole Algorithms (BHO) with a mutation technique called MBHO. Generally, BHO contains two major phases. At the exploitation phase, a set of stars are modified based on some rule and according to some objective function, the best star is selected as the black hole which attracts other stars. Furthermore, when a star gets closer to the event horizon, it will be swallowed and a new one will be randomly generated in the search space which thus is the exploration phase. However, randomness may cause the algorithm to fall into the trap of local optima, and to overcome such complications, inversion mutation is used. Furthermore, we modify a widely utilized objective function in most of the proposed works for wrapper feature selection by combining two new terms that are based on the correlation among the selected subset of features and the features and the classification label. We also utilize a transfer function, known as the V2 transfer function, to convert continuous values into discrete ones to enhance search. We assess our approach via extensive evaluation experiments using fourteen benchmark datasets. We benchmark the performance of a wrapper FS approach called Binary Cuckoo Search (BCS), and three filter-based FS approaches (namely Mutual Information Maximisation (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Relevance (mRMR)). Our evaluation has shown that the proposed model is an effective approach for FS, in selecting better features that enhance the performance metrics on the classifiers. Thus, MBHO can be utilized as one alternative to the existing state-of-art-approaches. We release the source codes of our implementation for the community to build on with new methods and datasets.

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Section: Optimization Problemmentioning

confidence: 99%

Investigating the Performance of a Novel Modified Binary Black Hole Optimization Algorithm for Enhancing Feature Selection

Al-Eiadeh,

Qaddoura,

Abdallah

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…For instance, the work [110] developed a K-means transition algorithm to enhance CS and BHO algorithms, exhibiting competitive performance in solving the set covering problem. Additionally, the work [111] proposed a multi-stage GWO approach to enhance feature selection for fund performance evaluation, overcoming local optima issues and reducing feature dimensions and classification error rates. Furthermore, the work [112] introduced a PSO-based feature selection method with explicit representation and feature grouping, addressing memory and computational challenges in PSO.…”

Section: Optimization Problemmentioning

confidence: 99%

Investigating the Performance of a Novel Modified Binary Black Hole Optimization Algorithm for Enhancing Feature Selection

Al-Eiadeh,

Qaddoura,

Abdallah

2024

Applied Sciences

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High-dimensional datasets often harbor redundant, irrelevant, and noisy features that detrimentally impact classification algorithm performance. Feature selection (FS) aims to mitigate this issue by identifying and retaining only the most pertinent features, thus reducing dataset dimensions. In this study, we propose an FS approach based on black hole algorithms (BHOs) augmented with a mutation technique termed MBHO. BHO typically comprises two primary phases. During the exploration phase, a set of stars is iteratively modified based on existing solutions, with the best star selected as the “black hole”. In the exploration phase, stars nearing the event horizon are replaced, preventing the algorithm from being trapped in local optima. To address the potential randomness-induced challenges, we introduce inversion mutation. Moreover, we enhance a widely used objective function for wrapper feature selection by integrating two new terms based on the correlation among selected features and between features and classification labels. Additionally, we employ a transfer function, the V2 transfer function, to convert continuous values into discrete ones, thereby enhancing the search process. Our approach undergoes rigorous evaluation experiments using fourteen benchmark datasets, and it is compared favorably against Binary Cuckoo Search (BCS), Mutual Information Maximization (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Eelevance (mRMR), approaches. The results demonstrate the efficacy of our proposed model in selecting superior features that enhance classifier performance metrics. Thus, MBHO is presented as a viable alternative to the existing state-of-the-art approaches. We make our implementation source code available for community use and further development.

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Strategic integration of adiabatic compressed air energy storage in urban buildings: Enhancing energy efficiency through gray wolf optimizer-enhanced dynamic simulation framework

Ali,

Basem,

Jasim

et al. 2024

Journal of Energy Storage

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Multiple strategies based Grey Wolf Optimizer for feature selection in performance evaluation of open-ended funds

Cited by 9 publications

References 48 publications

Investigating the Performance of a Novel Modified Binary Black Hole Optimization Algorithm for Enhancing Feature Selection

Investigating the Performance of a Novel Modified Binary Black Hole Optimization Algorithm for Enhancing Feature Selection

Investigating the Performance of a Novel Modified Binary Black Hole Optimization Algorithm for Enhancing Feature Selection

Strategic integration of adiabatic compressed air energy storage in urban buildings: Enhancing energy efficiency through gray wolf optimizer-enhanced dynamic simulation framework

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