Voting Algorithms Model with a Support Sets System by Class

Rodriguez Salas D, Lazo-Cortés MS, Mollineda RA, Olvera-López A, de la Calleja J, Benitez A (2014)


Publication Type: Conference contribution

Publication year: 2014

Publisher: Springer Nature

Edited Volumes: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Book Volume: 8857

Pages Range: 128-139

Conference Proceedings Title: MICAI 2014: Nature-Inspired Computation and Machine Learning

Event location: Tuxtla Gutierrez, Mexico

ISBN: 9783319136493

DOI: 10.1007/978-3-319-13650-9_12

Abstract

The voting algorithms model (AlVot) allows building supervised classification methods based in partial analogies. These algorithms use a collection of features subsets as support to classify a new object, which is called support set system. Each support set consists of selected features that are intended to discriminate the class of each object in the learning matrix. In this paper, a new model called AlVot By Class (AlVot BC) is proposed. It is aimed to build a support set system by class, so that each class-specific support set provides evidence of the membership of an object to the class represented by that support set. The classification performance of the proposed algorithm is evaluated on seven databases from the UCI Machine Learning Repository. The results show a clear improvement over its analogous algorithm based on AlVot.

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How to cite

APA:

Rodriguez Salas, D., Lazo-Cortés, M.S., Mollineda, R.A., Olvera-López, A., de la Calleja, J., & Benitez, A. (2014). Voting Algorithms Model with a Support Sets System by Class. In MICAI 2014: Nature-Inspired Computation and Machine Learning (pp. 128-139). Tuxtla Gutierrez, Mexico: Springer Nature.

MLA:

Rodriguez Salas, Dalia, et al. "Voting Algorithms Model with a Support Sets System by Class." Proceedings of the 13th Mexican International Conference on Artificial Intelligence (MICAI), Tuxtla Gutierrez, Mexico Springer Nature, 2014. 128-139.

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