Name
Title | ||
|---|---|---|
Two-stage approach to feature set optimization for unsupervised dataset with heterogeneous attributes |
Abstract | ||
|---|---|---|
•A new unsupervised feature selection method is proposed for heterogeneous dataset.•Relevant and non-redundant features are selected without prior data transformation.•The proposed algorithm is suitable for high dimensional data.•The proposed algorithm is scalable with respect to any size of data.•Rigorous comparative study carried out to prove efficacy of proposed mechanism. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.eswa.2021.114563 | Expert Systems with Applications |
Keywords | DocType | Volume |
Feature selection,Feature ranking,Normalized mutual information,Unsupervised learning,Hybrid feature set optimization | Journal | 172 |
ISSN | Citations | PageRank |
0957-4174 | 2 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arpita Chaudhuri | 1 | 2 | 0.35 |
| Debasis Samanta | 2 | 227 | 37.98 |
| Monalisa Sarma | 3 | 10 | 5.24 |