Abstract
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.
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© 2001 Springer-Verlag Berlin Heidelberg
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Campos, T.E., Bloch, I., Cesar, R.M. (2001). Feature Selection Based on Fuzzy Distances Between Clusters: First Results on Simulated Data. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_19
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DOI: https://doi.org/10.1007/3-540-44732-6_19
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