Whale Optimization Algorithm for High-dimensional Small-Instance Feature Selection

Mafarja, Majdi; Jaber, Iyad; Ahmed, Sobhi

doi:10.1109/isiict.2018.8613293

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…In a wrapper feature selection model, Mafarja et al [39] proposed two variants of the Whale Optimization Algorithm (WOA), called SWOA and VWOA. A dataset is minimized by removing redundant or irrelevant features in order to enhance the learning algorithms.…”

Section: Related Work Based On the Wrapper Approachmentioning

confidence: 99%

“…There have been several articles that have discussed combining the WOA method with other methods in order to reduce execution time, but these efforts have not been successful. We can point out some of the main negative points in Table 10, including the lack of scalability for large datasets ( [32], [44] and [53]), the risk of over tting ([48] and [49]), not considering the dependency of features ([46] and [52]) and the need for additional computing power ( [37], [39] and [51]).…”

Section: Nbmentioning

confidence: 99%

“…[36], [37], [38], [39], [40], [41], [42], [43], [44] Filter There is no expectation that every metaheuristic feature selection algorithm will work well for every problem. In this regard, it is important to report underperforming results as well.…”

Section: • Computational Timementioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Survey of Feature Selection Techniques based on Whale Optimization Algorithm

Ebrahimabadi

Mansouri

2022

Preprint

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A large number of features is the main problem in big data, leading to the curse of dimensionality. Meanwhile, feature selection is suggested as a solution. The process of feature selection consists of adding relevant features to a neural model and eliminating irrelevant or redundant ones. The feature selection community has recently been drawn to swarm intelligence techniques due to their simplicity and potential global search capabilities. A straightforward overview of the newest research in the feature selection field is provided here using a nature-inspired metaheuristic method called Whale Optimization Algorithm (WOA). Research is expected to be presented in terms of various types of state-of-the-art methods and their advantages and disadvantages, encouraging researchers to investigate more advanced approaches. A discussion of possible limitations and issues for future research is included as well as guidance for practitioners on selecting appropriate methods for real-world situations.

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Section: Related Work Based On the Wrapper Approachmentioning

confidence: 99%