Name
Abstract | ||
|---|---|---|
In data mining and knowledge discovery, the curse of dimensionality is a damning factor for numerous potentially powerful machine learning techniques, while rough set theory can be employed to reduce the dimensionality of datasets as a preprocessing step. For rough set based methods, finding reducts is an essential step, yet it is of high complexity. In this paper, based on particle swarm optimization(PSO) which is an optimization algorithm inspired by social behavior of flocks of birds when they are searching for food, a novel method is proposed for finding useful features instead of reducts in rough set theory. Subsequent experiments on UCI show that this method performs well on whole convergence, and can retrieve useful subsets effectively while retaining attributes of high importance as possible |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11795131_84 | RSKT |
Keywords | Field | DocType |
particle swarm optimization,optimization algorithm,novel method,useful feature,high importance,rough set theory,rough set,essential step,high complexity,preprocessing step,curse of dimensionality,knowledge discovery,machine learning,social behavior,data mining | Particle swarm optimization,Data mining,Evolutionary algorithm,Feature selection,Computer science,Swarm intelligence,Curse of dimensionality,Rough set,Artificial intelligence,Knowledge extraction,Artificial neural network,Machine learning | Conference |
Volume | ISSN | ISBN |
4062 | 0302-9743 | 3-540-36297-5 |
Citations | PageRank | References |
1 | 0.49 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongsheng Zhao | 1 | 75 | 19.66 |
| Xiaofeng Zhang | 2 | 44 | 4.84 |
| Shixiang Jia | 3 | 5 | 1.62 |
| Fuzeng Zhang | 4 | 6 | 2.30 |