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...
Main Authors: | Nur Atiqah, Mustapa, Azlyna, Senawi, Wei, Hua-Liang |
---|---|
Format: | Article |
Language: | English |
Published: |
Penerbit UMP
2023
|
Subjects: | |
Online Access: | http://umpir.ump.edu.my/id/eprint/40090/1/Supervised%20Feature%20Selection%20based%20on%20the%20Law%20of%20Total%20Variance.pdf |
Similar Items
-
Feature selection using law of total variance with fast correlation-based filter
by: Nur Atiqah, Mustapa, et al.
Published: (2023) -
Computational efficiency of generalized variance and vector variance
by: Sharif, Shamshuritawati, et al.
Published: (2014) -
Performance of selected imputation techniques for missing variances in meta-analysis
by: Nik Idris, Nik Ruzni, et al.
Published: (2013) -
Identifying the Ideal Number Q-Components of the Bayesian Principal Component Analysis Model for Missing Daily Precipitation Data Treatment
by: Chuan, Zun Liang, et al.
Published: (2018) -
The efficiency of average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling in identifying homogeneous precipitation catchments
by: Chuan, Zun Liang, et al.
Published: (2018)