Accurate and fast prototype selection based on the notion of relevant and border prototypes

J. Arturo Olvera-López, J. Ariel Carrasco-Ochoa, J. Franciso Martínez-Trinidad

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

© 2018-IOS Press and the authors. All rights reserved. In supervised classification, a training set is given to a classifier to learn a decision rule for classifying unseen cases. When large training sets are processed, the training stage becomes slow especially for instance-based learning. However, not all information in a training set is useful for classification because it could contain either redundant or noisy prototypes. Therefore a process for discarding useless prototypes is required; this process is known as prototype selection. In this work, we present some methods for selecting prototypes based on prototype relevance, which are accurate and fast for large datasets; in addition, our methods can be applied over datasets described by nominal features.We report experimental results showing the effectiveness of our methods as well as a comparison against other successful prototype selection methods.
Original languageAmerican English
Pages (from-to)2923-2934
Number of pages2629
JournalJournal of Intelligent and Fuzzy Systems
DOIs
StatePublished - 1 Jan 2018
Externally publishedYes

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Prototype
Classifiers
Instance-based Learning
Addition method
Supervised Classification
Decision Rules
Large Data Sets
Categorical or nominal
Classifier
Training
Experimental Results

Cite this

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title = "Accurate and fast prototype selection based on the notion of relevant and border prototypes",
abstract = "{\circledC} 2018-IOS Press and the authors. All rights reserved. In supervised classification, a training set is given to a classifier to learn a decision rule for classifying unseen cases. When large training sets are processed, the training stage becomes slow especially for instance-based learning. However, not all information in a training set is useful for classification because it could contain either redundant or noisy prototypes. Therefore a process for discarding useless prototypes is required; this process is known as prototype selection. In this work, we present some methods for selecting prototypes based on prototype relevance, which are accurate and fast for large datasets; in addition, our methods can be applied over datasets described by nominal features.We report experimental results showing the effectiveness of our methods as well as a comparison against other successful prototype selection methods.",
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Accurate and fast prototype selection based on the notion of relevant and border prototypes. / Olvera-López, J. Arturo; Carrasco-Ochoa, J. Ariel; Martínez-Trinidad, J. Franciso.

In: Journal of Intelligent and Fuzzy Systems, 01.01.2018, p. 2923-2934.

Research output: Contribution to journalArticleResearchpeer-review

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