Name
Abstract | ||
|---|---|---|
This paper presents a neuro-fuzzy approach for diagnosis of antibody deficiency syndrome, where a new neuro-fuzzy network with fuzzy activation functions (FAFs) at hidden layer is used. The FAFs capturing some essential information on pattern distributions, can be adaptively constructed using training examples. To improve the generalization capability and reduce the model complexity, a heuristic method for feature selection is proposed by measuring the size of non-overlapped areas of the FAFs. The effectiveness of our proposed techniques is investigated by an immunology clinical data set collected from the University of California, Irvine (UCI) immunology laboratory. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.neucom.2005.06.009 | Neurocomputing |
Keywords | Field | DocType |
Neuro-fuzzy networks,Fuzzy activation functions,Medical diagnosis,Feature selection | Heuristic,Neuro-fuzzy,Feature selection,Pattern recognition,Antibody deficiency syndrome,Computer science,Fuzzy logic,Artificial intelligence,Machine learning,Medical diagnosis,Model complexity | Journal |
Volume | Issue | ISSN |
69 | 7 | 0925-2312 |
Citations | PageRank | References |
14 | 1.09 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joon Shik Lim | 1 | 51 | 6.39 |
| Dianhui Wang | 2 | 1547 | 93.41 |
| Yong Soo Kim | 3 | 185 | 23.42 |
| Sudhir Gupta | 4 | 26 | 2.78 |