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Title: Prototype Generation for Multi-label Nearest Neighbours Classification
Authors: Ougiaroglou, Stefanos
Filippakis, Panagiotis
Evangelidis, Georgios
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Multi-label classification
Data Reduction
Prototype Generation
k-NN Classification
Binary Relevance
Issue Date: 15-Sep-2021
Volume: 12886
First Page: 172
Last Page: 183
Volume Title: Hybrid Artificial Intelligent Systems
Part of Series: Lecture Notes in Computer Science
Part of Series: Lecture Notes in Computer Science
Abstract: Numerous Prototype Selection and Generation algorithms for instance based classifiers and single label classification problems have been proposed in the past and are available in the literature. They build a small set of prototypes that represents as best as possible the initial training data. This set is called the condensing set and has the benefit of low computational cost while preserving accuracy. However, the proposed Prototype Selection and Generation algorithms are not applicable to multi-label problems where an instance may belong to more than one classes. The popular Binary Relevance transformation method is also inadequate to be combined with a Prototype Selection or Generation algorithm because of the multiple binary condensing sets it builds. Reduction through Homogeneous Clustering (RHC) is a simple, fast, parameter-free single label Prototype Generation algorithm that is based on k-means clustering. This paper proposes a RHC variation for multi-label training datasets. The proposed method, called Multi-label RHC (MRHC), inherits all the aforementioned desirable properties of RHC and generates multi-label prototypes. The experimental study based on nine multi-label datasets shows that MRHC achieves high reduction rates without negatively affecting accuracy.
ISBN: 978-3-030-86270-1
ISSN: 0302-9743
Other Identifiers: 10.1007/978-3-030-86271-8_15
Appears in Collections:Department of Applied Informatics

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