Name
Abstract | ||
|---|---|---|
In this paper a methodology using evolutionary algorithms is introduced for the optimization of fuzzy classifiers based on B-splines. The proposed algorithm maximizes the performance and minimizes the size of the classifier. On a well-known classification problem the algorithm performs an input selection over 9 observed characteristics yielding in a statement which attributes are important with respect to diagnose malignant or benign type of cancer. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/3-540-45493-4_42 | Fuzzy Days |
Keywords | Field | DocType |
well-known classification problem,computational intelligence,benign type,optimizing fuzzy,fuzzy classifier,input selection,evolutionary algorithm,proposed algorithm,observed characteristic | B-spline,Input selection,Computational intelligence,Evolutionary algorithm,Computer science,Fuzzy logic,Artificial intelligence,Fuzzy control system,Classifier (linguistics),Machine learning,Genetic algorithm | Conference |
Volume | ISSN | ISBN |
2206 | 0302-9743 | 3-540-42732-5 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ingo Renners | 1 | 7 | 3.03 |
| Adolf Grauel | 2 | 34 | 9.56 |
| Ernesto Saavedra | 3 | 0 | 0.68 |