Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
409573 | Neurocomputing | 2006 | 6 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Joon Shik Lim, Dianhui Wang, Yong-Soo Kim, Sudhir Gupta,