Robust ensemble feature selection for high dimensional data sets

2013 ◽  
Author(s):  
Afef Ben Brahim ◽  
Mohamed Limam
Keyword(s):  
Feature Selection ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Data Sets ◽  
Selection For

scholarly journals Feature Selection for High Dimensional Data Using Weighted K-Nearest Neighbors and Genetic Algorithm

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 139512-139528
Author(s):  
Shuangjie Li ◽  
Kaixiang Zhang ◽  
Qianru Chen ◽  
Shuqin Wang ◽  
Shaoqiang Zhang
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
High Dimensional Data ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
K Nearest Neighbors ◽  
Selection For

scholarly journals A Novel Granularity Optimal Feature Selection based on Multi-Variant Clustering for High Dimensional Data

2021 ◽  
Vol 12 (3) ◽  
pp. 5051-5062
Author(s):  
Srinivas Kolli Et. al.
Keyword(s):  
Feature Selection ◽  
High Dimensional Data ◽  
Classification Performance ◽  
High Dimensional ◽  
Second Phase ◽  
Data Sets ◽  
Aggressive Approach ◽  
Related Data ◽  
Optimal Feature ◽  
Selection Of

Clustering is the most complex in multi/high dimensional data because of sub feature selection from overall features present in categorical data sources. Sub set feature be the aggressive approach to decrease feature dimensionality in mining of data, identification of patterns. Main aim behind selection of feature with respect to selection of optimal feature and decrease the redundancy. In-order to compute with redundant/irrelevant features in high dimensional sample data exploration based on feature selection calculation with data granular described in this document. Propose aNovel Granular Feature Multi-variant Clustering based Genetic Algorithm (NGFMCGA) model to evaluate the performance results in this implementation. This model main consists two phases, in first phase, based on theoretic graph grouping procedure divide features into different clusters, in second phase, select strongly  representative related feature from each cluster with respect to matching of subset of features. Features present in this concept are independent because of features select from different clusters, proposed approach clustering have high probability in processing and increasing the quality of independent and useful features.Optimal subset feature selection improves accuracy of clustering and feature classification, performance of proposed approach describes better accuracy with respect to optimal subset selection is applied on publicly related data sets and it is compared with traditional supervised evolutionary approaches

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