Neuro-fuzzy feature evaluation with theoretical analysis

De, R. K. ; Basak, J. ; Pal, S. K. (1999) Neuro-fuzzy feature evaluation with theoretical analysis Neural Networks, 12 (10). pp. 1429-1455. ISSN 0893-6080

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The article provides a fuzzy set theoretic feature evaluation index and a connectionist model for its evaluation along with their theoretical analysis. A concept of weighted membership function is introduced which makes the modeling of the class structures more appropriate. A neuro-fuzzy algorithm is developed for determining the optimum weighting coefficients representing the feature importance. It is shown theoretically that the evaluation index has a fixed upper bound and a varying lower bound, and it monotonically increases with the lower bound. A relation between the evaluation index, interclass distance and weighting coefficients is established. Effectiveness of the algorithms for evaluating features both individually and in a group (considering their independence and dependency) is demonstrated along with comparisons on speech, Iris, medical and mango-leaf data. The results are also validated using scatter diagram and k-NN classifier.

Item Type:Article
Source:Copyright of this article belongs to International Neural Network Society.
Keywords:Fuzzy Sets; Neural Networks; Pattern Recognition; Feature Evaluation Index; Softcomputing; Weighted Membership Function
ID Code:26110
Deposited On:06 Dec 2010 13:05
Last Modified:17 May 2016 09:27

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