Genetic Approach to Support Sets System Estimation for ALVOT

J.A. Carrasco-Ochoa and J.F. Martínez-Trinidad (Mexico)


genetic algorithms, logical combinatorial pattern recognition, supervised classification, partial precedence.


In this paper we propose an alternative method, based on genetic algorithms, to estimate the support sets system for the supervised classification model ALVOT. Usually, this system is taken as the set of all typical testors, but this has a problem: algorithms for calculating all typical testor have very high complexity. Also, in some practical cases, the number of typical testors can be too large. This can become typical testors inapplicable for the classification stage. The proposed method allows generating support sets systems of limited size, but with a high efficiency for classification. Some performance examples, of the new method, are exposed. Quality for classification, of the results, is compared against typical testors option.

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