Core-Generating Discretization for Rough Set Feature Selection

2011 ◽  
pp. 135-158 ◽  
Author(s):  
David Tian ◽  
Xiao-jun Zeng ◽  
John Keane
Keyword(s):  
Feature Selection ◽  
Rough Set

scholarly journals Rough set-based feature selection for weakly labeled data

2021 ◽  
Author(s):  
Andrea Campagner ◽  
Davide Ciucci ◽  
Eyke Hüllermeier
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Selection For ◽  
Weakly Labeled Data

scholarly journals An Ensemble Classification Method for High-Dimensional Data Using Neighborhood Rough Set

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jing Zhang ◽  
Guang Lu ◽  
Jiaquan Li ◽  
Chuanwen Li
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Small Sample Size ◽  
High Dimensional Data ◽  
Classification Performance ◽  
Small Sample ◽  
Ensemble Classification ◽  
High Dimensional ◽  
Sample Classification ◽  
Neighborhood Rough Set

Mining useful knowledge from high-dimensional data is a hot research topic. Efficient and effective sample classification and feature selection are challenging tasks due to high dimensionality and small sample size of microarray data. Feature selection is necessary in the process of constructing the model to reduce time and space consumption. Therefore, a feature selection model based on prior knowledge and rough set is proposed. Pathway knowledge is used to select feature subsets, and rough set based on intersection neighborhood is then used to select important feature in each subset, since it can select features without redundancy and deals with numerical features directly. In order to improve the diversity among base classifiers and the efficiency of classification, it is necessary to select part of base classifiers. Classifiers are grouped into several clusters by k-means clustering using the proposed combination distance of Kappa-based diversity and accuracy. The base classifier with the best classification performance in each cluster will be selected to generate the final ensemble model. Experimental results on three Arabidopsis thaliana stress response datasets showed that the proposed method achieved better classification performance than existing ensemble models.

A rough set approach to feature selection based on power set tree

2011 ◽  
Vol 24 (2) ◽  
pp. 275-281 ◽  
Author(s):  
Yumin Chen ◽  
Duoqian Miao ◽  
Ruizhi Wang ◽  
Keshou Wu
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Power Set

Online early terminated streaming feature selection based on Rough Set theory

2021 ◽  
pp. 107993
Author(s):  
Peng Zhou ◽  
Peipei Li ◽  
Shu Zhao ◽  
Yanping Zhang
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory
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