title: One-hot vector hybrid associative classifier for medical data classification.
abstract: Pattern recognition and classification are two of the key topics in computer science. In this paper a novel method for the task of pattern classification is presented. The proposed method combines a hybrid associative classifier (Clasificador Híbrido Asociativo con Traslación, CHAT, in Spanish), a coding technique for output patterns called one-hot vector and majority voting during the classification step. The method is termed as CHAT One-Hot Majority (CHAT-OHM). The performance of the method is validated by comparing the accuracy of CHAT-OHM with other well-known classification algorithms. During the experimental phase, the classifier was applied to four datasets related to the medical field. The results also show that the proposed method outperforms the original CHAT classification accuracy.
authors.affiliation: Neural Networks and Unconventional Computating Lab/Alpha-Beta Group, Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México, Distrito Federal, México.
authors.forename: Abril Valeria
authors.initials: AV
authors.lastname: Uriarte-Arcia
authors.affiliation: Intelligent Computing Lab/Alpha-Beta Group, Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Ciudad de México, Distrito Federal, México.
authors.forename: Itzamá
authors.initials: I
authors.lastname: López-Yáñez
authors.affiliation: Neural Networks and Unconventional Computating Lab/Alpha-Beta Group, Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México, Distrito Federal, México.
authors.forename: Cornelio
authors.initials: C
authors.lastname: Yáñez-Márquez
mesh_terms: D000465:Algorithms; D060388:Support Vector Machine
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