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A Hybrid Correlation-PSO Feature Selection Framework with Extreme Learning Machines for High-Accuracy Cancer Classification in Microarray Data

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Title: A Hybrid Correlation-PSO Feature Selection Framework with Extreme Learning Machines for High-Accuracy Cancer Classification in Microarray Data
Authors: Ali, Md. Anwar, Devasani, Vaishnapallavi, Vardhan, Soma Harsha, Reethu, Golla, Vishnu, Gotrala
Source: 2026 International Conference on Visual Analytics and Data Visualization (ICVADV) Visual Analytics and Data Visualization (ICVADV), 2026 International Conference on. :717-722 Feb, 2026
Relation: 2026 International Conference on Visual Analytics and Data Visualization (ICVADV)
Database: IEEE Xplore Digital Library
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DbLabel: IEEE Xplore Digital Library
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PubType: Conference
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  Data: A Hybrid Correlation-PSO Feature Selection Framework with Extreme Learning Machines for High-Accuracy Cancer Classification in Microarray Data
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  Data: 2026 International Conference on Visual Analytics and Data Visualization (ICVADV) Visual Analytics and Data Visualization (ICVADV), 2026 International Conference on. :717-722 Feb, 2026
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RecordInfo BibRecord:
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        Value: 10.1109/ICVADV67766.2026.11470441
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        PageCount: 6
        StartPage: 717
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      – TitleFull: A Hybrid Correlation-PSO Feature Selection Framework with Extreme Learning Machines for High-Accuracy Cancer Classification in Microarray Data
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            NameFull: Ali, Md. Anwar
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            NameFull: Devasani, Vaishnapallavi
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            NameFull: Vardhan, Soma Harsha
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            NameFull: Reethu, Golla
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            NameFull: Vishnu, Gotrala
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              Type: published
              Y: 2026
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