Unsupervised feature selection using a neuro-fuzzy approach

Basak, Jayanta ; De, Rajat K. ; Pal, Sankar K. (1998) Unsupervised feature selection using a neuro-fuzzy approach Pattern Recognition Letters, 19 (11). pp. 997-1006. ISSN 0167-8655

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Official URL: http://linkinghub.elsevier.com/retrieve/pii/S01678...

Related URL: http://dx.doi.org/10.1016/S0167-8655(98)00083-X


A neuro-fuzzy methodology is described which involves connectionist minimization of a fuzzy feature evaluation index with unsupervised training. The concept of a flexible membership function incorporating weighed distance is introduced in the evaluation index to make the modeling of clusters more appropriate. A set of optimal weighing coefficients in terms of networks parameters representing individual feature importance is obtained through connectionist minimization. Besides, the investigation includes the development of another algorithm for ranking of different feature subsets using the aforesaid fuzzy evaluation index without neural networks. Results demonstrating the effectiveness of the algorithms for various real life data are provided.

Item Type:Article
Source:Copyright of this article belongs to International Association for Pattern Recognition.
ID Code:26067
Deposited On:06 Dec 2010 13:09
Last Modified:17 May 2016 09:25

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