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
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.
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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