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 classication. A wrapper-style search guided by a genetic algorithm uses the Hybrid Associative Classier to evaluate(More)
The present work describes an original associative model of pattern classification and its application to align different ontologies containing Learning Objects (LOs), which are in turn related to Open and Distance Learning (ODL) educative content. The problem of aligning ontologies is known as Ontology Matching Problem (OMP), whose solution is modeled in(More)