A Hybrid Feature Selection Algorithm Based on Collision Principle and Adaptability Article Swipe
X. H. Bai
,
Yuefeng Zheng
,
Yang Lu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3437738
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3437738
Feature selection plays a significant role in machine learning and data mining, where the goal is to screen out the most representative and relevant subset of features from a large collection of features to improve the performance and generalization ability of the model. In this paper, a hybrid feature selection algorithm that combines a filter algorithm and an improved particle swarm optimization algorithm is proposed, that is, the Information Gain and Maximum Pearson Minimum Mutual Information improved Adaptive Particle Swarm Optimization algorithm (IGMPMMIAPSO). First, combined with the characteristics of the Pearson correlation coefficient and mutual information, a filter algorithm called Maximum Pearson Minimum Mutual Information (MPMMI) is proposed. The algorithm balances the relevance and redundancy between the features by adjusting two weight parameters ( and ). Second, Adaptive Adjustment of Control (AAC) is introduced to update the particle swarm optimization algorithm, so that the particle velocity has a higher searching ability, and the diversity of population position changes is increased. The improved algorithm was used as the wrapper algorithm. Simultaneously, the concepts of the No Continuous Change (NCC) times and collision distance values are proposed. According to these, the IGMPMMIAPSO algorithm is proposed by combining the filter algorithm and wrapper algorithm. To verify the performance of the proposed algorithm, we experimented with other state-of-the-art hybrid algorithms using eight datasets. The experimental results show that the classification accuracy of the proposed algorithm is at least 0.1% higher than that of the other five algorithms, and the feature subset length is shorter.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3437738
- OA Status
- gold
- Cited By
- 2
- References
- 34
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- 10
- OpenAlex ID
- https://openalex.org/W4401246653
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401246653Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3437738Digital Object Identifier
- Title
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A Hybrid Feature Selection Algorithm Based on Collision Principle and AdaptabilityWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
X. H. Bai, Yuefeng Zheng, Yang LuList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2024.3437738Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1109/access.2024.3437738Direct OA link when available
- Concepts
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Algorithm, Particle swarm optimization, Feature selection, Mutual information, Redundancy (engineering), Mathematics, Population, Feature (linguistics), Computer science, Artificial intelligence, Sociology, Operating system, Linguistics, Philosophy, DemographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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34Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.</tex-math></inline-formula>). | 131 |
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