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Title
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ATISA: Adaptive Threshold-based Instance Selection Algorithm
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Author
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Abstract
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| 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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Language
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English
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Source (journal)
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Expert systems with applications. - New York
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Publication
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New York
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2013
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ISSN
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0957-4174
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DOI
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10.1016/J.ESWA.2013.06.053
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Volume/pages
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40
:17
(2013)
, p. 6894-6900
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ISI
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000326214700023
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Full text (Publisher's DOI)
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