Name
Abstract | ||
|---|---|---|
In this paper, we propose a new nonlinear classifier based on a generalized Choquet integral with signed fuzzy measures to enhance the classification power by capturing all possible interactions among two or more attributes. A special genetic algorithm is designed to implement this classification optimization with fast convergence. Instead of using a discrete misclassification rate, the objective function to be optimized in this research is a continuous Choquet distance with a penalty coefficient for misclassified points. The numerical experiment shows that the special genetic algorithm effectively solves the nonlinear classification problem and this nonlinear classifier accurately identifies classes. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/FUZZY.2007.4295577 | FUZZ-IEEE |
Keywords | Field | DocType |
fuzzy set theory,signed fuzzy measure,discrete misclassification rate,genetic algorithm,genetic algorithms,choquet integral,algorithm design and analysis,pattern recognition,objective function,training data,mathematical model,design optimization,convergence,fuzzy sets | Fuzzy classification,Fuzzy set operations,Computer science,Fuzzy logic,Fuzzy measure theory,Fuzzy set,Artificial intelligence,Choquet integral,Fuzzy number,Membership function,Machine learning | Conference |
ISSN | ISBN | Citations |
1098-7584 E-ISBN : 1-4244-1210-2 | 1-4244-1210-2 | 2 |
PageRank | References | Authors |
0.47 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Honggang Wang | 1 | 1365 | 124.06 |
| Hua Fang | 2 | 343 | 32.48 |
| Hamid Sharif | 3 | 1027 | 93.17 |
| Zhenyuan Wang | 4 | 684 | 90.22 |