Feature selection for binary classification based on class labeling, SOM, and hierarchical clustering
Feature selection plays an important role in algorithms for processing high-dimensional data. Traditional pattern classification and information theory methods are widely applied to feature selection methods. However, traditional pattern classification methods such as Fisher Score, Laplacian Score,...
Main Authors: | Zhao Zhengtian, Rui Zhiyuan, Duan Xiaoyan |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2023-11-01
|
Series: | Measurement + Control |
Online Access: | https://doi.org/10.1177/00202940231173748 |
Similar Items
-
Feature extraction with spectral clustering for gene function prediction using hierarchical multi-label classification
by: Miguel Romero, et al.
Published: (2022-05-01) -
Spherical Tree-Structured SOM and Its Application to Hierarchical Clustering
by: Koki Yoshioka, et al.
Published: (2022-07-01) -
Selection of informative clusters from hierarchical cluster tree with gene classes
by: Toronen Petri
Published: (2004-03-01) -
Feature Selection for Binary Classification Within Functional Genomics Experiments via Interquartile Range and Clustering
by: Zardad Khan, et al.
Published: (2019-01-01) -
Label Metric for Multi-Class Multi-Target Tracking under Hierarchical Multilevel Classification
by: Jingdong Diao, et al.
Published: (2022-11-01)