Mario Aldape-Pérez

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Classification is one of the key issues in medical diagnosis. In this paper, a novel approach to perform pattern classification tasks is presented. This model is called Associative Memory based Classifier (AMBC). Throughout the experimental phase, the proposed algorithm is applied to help diagnose diseases; particularly, it is applied in the diagnosis of(More)
Performance in most pattern classifiers is improved when redundant or irrelevant features are removed, however, this is mainly achieved by successive classifiers construction. In this paper hybrid classification and masking techniques are presented as a new feature selection approach. The algorithm uses a hybrid classifier to provide a mask that identifies(More)
Feature selection aims to nd ways to single out the subset of features which best represents the phenomenon at hand and improves performance. This paper presents an approach based on evolutionary computation and the associative paradigm for classi cation. A wrapperstyle search guided by a genetic algorithm uses the Hybrid Associative Classi er to evaluate(More)
Resumen.Las memorias asociativas tienen una serie de caracteŕısticas, incluyendo un rápido y eficiente método de clasificación, aśı como una tolerancia intŕınseca al ruido que las hace ideales para gran variedad de aplicaciones. En este art́ıculo se utilizarán las memorias alfa-beta autoasociativas con el propósito de recomendar un herbicida en base a(More)
Performance of most pattern classifiers is improved when redundant or irrelevant features are removed. Nevertheless, this is mainly achieved by highly demanding computational methods or successive classifiers’ construction. This paper shows how the associative memory paradigm and parallel computing can be used to perform Feature Selection tasks. This(More)