Name
Title | ||
|---|---|---|
A filter-based bare-bone particle swarm optimization algorithm for unsupervised feature selection |
Abstract | ||
|---|---|---|
Due to good exploration capability, particle swarm optimization (PSO) has shown advantages on solving supervised feature selection problems. Compared with supervised and semi-supervised cases, unsupervised feature selection becomes very difficult as a result of no label information. This paper studies a novel PSO-based unsupervised feature selection method, called filter-based bare-bone particle swarm optimization algorithm (FBPSO). Two filter-based strategies are proposed to speed up the convergence of the algorithm. One is a space reduction strategy based on average mutual information, which is used to remove irrelevant and weakly relevant features fast; another is a local filter search strategy based on feature redundancy, which is used to improve the exploitation capability of the swarm. And, a feature similarity-based evaluation function and a parameter-free update strategy of particle are introduced to enhance the performance of FBPSO. Experimental results on some typical datasets confirm superiority and effectiveness of the proposed FBPSO. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s10489-019-01420-9 | Applied Intelligence |
Keywords | Field | DocType |
Particle swarm optimization, Feature selection, Unsupervised | Convergence (routing),Particle swarm optimization,Reduction strategy,Feature selection,Pattern recognition,Swarm behaviour,Computer science,Algorithm,Evaluation function,Redundancy (engineering),Artificial intelligence,Mutual information | Journal |
Volume | Issue | ISSN |
49 | 8 | 0924-669X |
Citations | PageRank | References |
5 | 0.39 | 29 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Zhang | 1 | 438 | 103.95 |
| Hai-Gang Li | 2 | 5 | 0.39 |
| Qing Wang | 3 | 345 | 76.64 |
| Chao Peng | 4 | 6 | 0.75 |