Floating search methods in feature selection
Pattern Recognition Letters
Digital image processing
Towards general measures of comparison of objects
Fuzzy Sets and Systems - Special issue dedicated to the memory of Professor Arnold Kaufmann
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distances between fuzzy sets representing grey level images
Fuzzy Sets and Systems
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Improved Face/spl times/Non-Face Discrimination using Fourier Descriptors through Feature Selection
SIBGRAPI '00 Proceedings of the 13th Brazilian Symposium on Computer Graphics and Image Processing
Automatic window design for gray-scale image processing based on entropy minimization
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
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Automatic feature selection methods are important in many situations where a large set of possible features are available from which a subset should be selected in order to compose suitable feature vectors. Several methods for automatic feature selection are based on two main points: a selection algorithm and a criterion function. Many criterion functions usually adopted depend on a distance between the clusters, being extremely important to the final result. Most distances between clusters are more suitable to convex sets, and do not produce good results for concave clusters, or for clusters presenting overlapping areas, in order to circumvent these problems, this paper presents a new approach using a criterion function based on a fuzzy distance. In our approach, each cluster is fuzzified and a fuzzy distance is applied to the fuzzy sets. Experimental results illustrating the advantages of the new approach are discussed.