Publication
Title
ATISA: Adaptive Threshold-based Instance Selection Algorithm
Author
Abstract
Instance reduction techniques can improve generalization, reduce storage requirements and execution time of instance-based learning algorithms. This paper presents an instance reduction algorithm called Adaptive Threshold-based Instance Selection Algorithm (ATISA). ATISA aims to preserve important instances based on a selection criterion that uses the distance of each instance to its nearest enemy as a threshold. This threshold defines the coverage area of each instance that is given by a hyper-sphere centered at it. The experimental results show the effectiveness, in terms of accuracy, reduction rate, and computational time, of the ATISA algorithm when compared with state-of-the-art reduction algorithms. (C) 2013 Elsevier Ltd. All rights reserved.
Language
English
Source (journal)
Expert systems with applications. - New York
Publication
New York : 2013
ISSN
0957-4174
Volume/pages
40:17(2013), p. 6894-6900
ISI
000326214700023
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
[E?say:metaLocaldata.cgzprojectinf]
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 04.12.2014
Last edited 04.10.2017
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