Paper Info
Title
Improved feature selection and classification by the 2-additive fuzzy measure
Abstract
This paper focusses on the investigation of a pattern recognition method based on the fuzzy integral. Until now this method has used a general fuzzy measure, which is characterized by exponential complexity. Naturally this led to some difficulties in practical applications of this pattern recognition method. In this paper, a heuristic algorithm for the identification of the 2-additive fuzzy measure, which is a particular type of k-additive fuzzy measures, is proposed. This algorithm can be used to reduce complexity of feature selection and classifier design. A further topic considered in this paper is the development of a feature selection algorithm for the fuzzy integral classifier. The proposed heuristic algorithm is based on two feature-evaluation criteria such as the importance and the interaction indexes. They were earlier defined in the literature using the semantic interpretation of the fuzzy measure. To validate the proposed algorithms, the feature selection algorithm and the pattern recognition method based on the fuzzy integral are applied to a problem of acoustic quality control.
Year
DOI
Venue
1999
10.1016/S0165-0114(98)00429-1
Fuzzy Sets and Systems
Keywords
Field
DocType
Pattern recognition,Feature selection,Fuzzy measure and integral theory
Neuro-fuzzy,Fuzzy classification,Defuzzification,Pattern recognition,Fuzzy set operations,Fuzzy logic,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy associative matrix,Fuzzy number,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
107
2
0165-0114
Citations 
PageRank 
References 
28
1.82
6
Authors
2
Name
Order
Citations
PageRank
L. Mikenina1576.91
H.-J. Zimmermann2312.93