Cited By
View all- Hlaváč JKopp MKohout JSkopal T(2023)Class Representatives Selection in Non-metric Spaces for Nearest Prototype ClassificationSimilarity Search and Applications10.1007/978-3-031-46994-7_10(111-124)Online publication date: 9-Oct-2023
Accurate and fast prototype selection based on the notion of relevant and border prototypes
Pages 2923 - 2934
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.
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Published: 01 January 2018
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View all- Hlaváč JKopp MKohout JSkopal T(2023)Class Representatives Selection in Non-metric Spaces for Nearest Prototype ClassificationSimilarity Search and Applications10.1007/978-3-031-46994-7_10(111-124)Online publication date: 9-Oct-2023