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Title: Prototype Selection for Multilabel Instance-Based Learning
Authors: Filippakis, Panagiotis
Ougiaroglou, Stefanos
Evangelidis, Georgios
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: data reduction techniques
instance reduction
multilabel classification
prototype selection
instance-based classification
binary relevance
Issue Date: Oct-2023
Source: Information
Volume: 14
Issue: 10
First Page: 572
Abstract: Reducing the size of the training set, which involves replacing it with a condensed set, is a widely adopted practice to enhance the efficiency of instance-based classifiers while trying to maintain high classification accuracy. This objective can be achieved through the use of data reduction techniques, also known as prototype selection or generation algorithms. Although there are numerous algorithms available in the literature that effectively address single-label classification problems, most of them are not applicable to multilabel data, where an instance can belong to multiple classes. Well-known transformation methods cannot be combined with a data reduction technique due to different reasons. The Condensed Nearest Neighbor rule is a popular parameter-free single-label prototype selection algorithm. The IB2 algorithm is the one-pass variation of the Condensed Nearest Neighbor rule. This paper proposes variations of these algorithms for multilabel data. Through an experimental study conducted on nine distinct datasets as well as statistical tests, we demonstrate that the eight proposed approaches (four for each algorithm) offer significant reduction rates without compromising the classification accuracy.
ISSN: 2078-2489
Other Identifiers: 10.3390/info14100572
Appears in Collections:Department of Applied Informatics

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