title: Neuro-fuzzy feature evaluation with theoretical analysis.
abstract: 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.
authors.affiliation: Machine Intelligence Unit, Indian Statistical Institute, Calcutta, India
authors.forename: R K.
authors.initials: RK
authors.lastname: De
authors.forename: J
authors.initials: J
authors.lastname: Basak
authors.forename: S K.
authors.initials: SK
authors.lastname: Pal