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Accurate and fast prototype selection based on the notion of relevant and border prototypes.
- Source :
-
Journal of Intelligent & Fuzzy Systems . 2018, Vol. 34 Issue 5, p2923-2934. 12p. - Publication Year :
- 2018
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10641246
- Volume :
- 34
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Journal of Intelligent & Fuzzy Systems
- Publication Type :
- Academic Journal
- Accession number :
- 129968528
- Full Text :
- https://doi.org/10.3233/JIFS-169478