Name
Abstract | ||
|---|---|---|
The paper presents a hybrid genetic search model (HGSM) with novel neighbourhood based uniform local search to select the subset of salient features removing redundant information from the universe of discourse. The method uses least square regression error as the fitness function for selecting the most feasible set of features from a large number of feature set. Proposed work is validated using our simulated character dataset and some real world datasets available in UCI Machine learning repository and performance comparison of proposed method with some other state of art feature selection methods are provided. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-20294-5_37 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Local search,Genetic algorithm,Data mining,Feature selection,Least square regression error | Least squares,Feature selection,Computer science,Fitness function,Feasible region,Neighbourhood (mathematics),Artificial intelligence,Local search (optimization),Machine learning,Genetic algorithm,Salient | Conference |
Volume | ISSN | Citations |
8947 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sunanda Das | 1 | 21 | 1.96 |
| Arka Ghosh | 2 | 53 | 6.09 |
| Asit Kumar Das | 3 | 73 | 16.06 |