Supervised feature selection based on the law of total variance
Feature selection is a fundamental pre-processing step in machine learning that decreases data dimensionality by removing superfluous and irrelevant features. This study proposes a supervised feature selection method based on feature relevance by employing the law of total variance (LTV). Specifical...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UMP
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/40090/1/Supervised%20Feature%20Selection%20based%20on%20the%20Law%20of%20Total%20Variance.pdf http://umpir.ump.edu.my/id/eprint/40090/ https://doi.org/10.15282/mekatronika.v5i2.9998 https://doi.org/10.15282/mekatronika.v5i2.9998 |
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