Please use this identifier to cite or link to this item:
Title: Data reduction via multi-label prototype generation
Authors: Ougiaroglou, Stefanos
Filippakis, Panagiotis
Fotiadou, Georgia
Evangelidis, Georgios
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Multi-label classification
Data Reduction Techniques
Prototype Generation
k-NN Classification
Binary Relevance
Issue Date: 14-Mar-2023
Publisher: Elsevier
Source: Neurocomputing
Volume: 526
First Page: 1
Last Page: 8
Abstract: A very common practice to speed up instance based classifiers is to reduce the size of their training set, that is, replace it by a condensing set, hoping that their accuracy will not worsen. This can be achieved by applying a Prototype Selection or Generation algorithm, also referred to as a Data Reduction Technique. Most of these techniques cannot be applied on multi-label problems, where an instance may belong to more than one classes. Reduction through Homogeneous Clustering (RHC) and Reduction by Space Partitioning (RSP3) are parameter-free single-label Prototype Generation algorithms. Both are based on recursive data partitioning procedures that identify homogeneous clusters of training data, which they replace by their representatives. This paper proposes variations of these algorithms for multi-label training datasets. The proposed methods generate multi-label prototypes and inherit all the desirable properties of their single-label versions. They consider clusters that contain instances that share at least one common label as homogeneous clusters. It is shown via an experimental study based on nine multi-label datasets that the proposed algorithms achieve good reduction rates without negatively affecting classification accuracy.
ISSN: 0925-2312
Other Identifiers: 10.1016/j.neucom.2023.01.004
Appears in Collections:Department of Applied Informatics

Files in This Item:
File Description SizeFormat 
  Until 2025-03-13
302,79 kBAdobe PDFView/Open Request a copy

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.