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Relevance and redundancy in fuzzy classification systems.

Ana Del Amo;  Daniel Gómez;  Javier Montero;  Gregory S. Biging — 2001

Mathware and Soft Computing

Fuzzy classification systems is defined in this paper as an aggregative model, in such a way that Ruspini classical definition of fuzzy partition appears as a particular case. Once a basic recursive model has been accepted, we then propose to analyze relevance and redundancy in order to allow the possibility of learning from previous experiences. All these concepts are applied to a real picture, showing that our approach allows to check quality of such a classification system.

Spectral fuzzy classification system: a supervised approach.

Ana Del Amo;  Daniel Gómez;  Javier Montero — 2003

Mathware and Soft Computing

The goal of this paper is to present all algorithm for pattern recognition, leveraging on an existing fuzzy clustering algorithm developed by Del Amo et al. [3, 5], and modifying it to its supervised version, in order to apply the algorithm to different pattern recognition applications in Remote Sensing. The main goal is to recognize the object and stop the search depending on the precision of the application. The referred algorithm was the core of a classification system based on Fuzzy Sets Theory...

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