Name
Abstract | ||
|---|---|---|
We propose an order-based Particle Swarm Optimization (o-PSO) hybrid algorithm for rough set approximate entropy reducts (oPSOAER). The o-PSO generates proper permutation of attributes, which are used by approximate entropy reduction algorithm to produce rough set reducts. The reducts are evaluated by fitness function. The primary criterion of optimization of the fitness function is the number of rules and the secondary is the reduct length. Our algorithm is tested on some UCI datasets. The results show that oSPOAER is efficient for approximate entropy reducts. The approximate entropy reducts optimized according to number of rules are better in classification algorithms than the shortest ones, and are much better for practical applications. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1543834.1543909 | GEC Summit |
Keywords | Field | DocType |
particle swarm optimization,approximate entropy reducts,hybrid algorithm,rough set reducts,rough set,uci datasets,fitness function,practical application,order-based particle swarm optimization,classification algorithm,approximate entropy reduction algorithm | Particle swarm optimization,Mathematical optimization,Approximate entropy,Reduct,Hybrid algorithm,Permutation,Multi-swarm optimization,Fitness function,Rough set,Artificial intelligence,Machine learning,Mathematics | Conference |
Citations | PageRank | References |
1 | 0.35 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangyang Wang | 1 | 35 | 4.98 |
| Wanggen Wan | 2 | 129 | 34.04 |
| Xiaoqing Yu | 3 | 75 | 11.53 |