Please use this identifier to cite or link to this item:
https://ruomo.lib.uom.gr/handle/7000/1164
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 RHC BRkNN |
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. |
URI: | https://doi.org/10.1007/978-3-030-86271-8_15 https://ruomo.lib.uom.gr/handle/7000/1164 |
ISBN: | 978-3-030-86270-1 978-3-030-86271-8 |
ISSN: | 0302-9743 1611-3349 |
Other Identifiers: | 10.1007/978-3-030-86271-8_15 |
Appears in Collections: | Department of Applied Informatics |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
2021_HAIS.pdf | 299,5 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.