Title
ATISA: Adaptive Threshold-based Instance Selection AlgorithmATISA: Adaptive Threshold-based Instance Selection Algorithm
Author
Faculty/Department
Faculty of Sciences. Physics
Research group
Department of Physics
Publication type
article
Publication
New York,
Subject
Economics
Mathematics
Computer. Automation
Source (journal)
Expert systems with applications. - New York
Volume/pages
40(2013):17, p. 6894-6900
ISSN
0957-4174
ISI
000326214700023
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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.
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