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Finding coverage using incremental attribute combinations

An, Jiyuan and Chen, Yi-Ping Phoebe 2009, Finding coverage using incremental attribute combinations, International journal of innovative computing, information and control, vol. 5, no. 5, pp. 1419-1428.

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Title Finding coverage using incremental attribute combinations
Author(s) An, Jiyuan
Chen, Yi-Ping Phoebe
Journal name International journal of innovative computing, information and control
Volume number 5
Issue number 5
Start page 1419
End page 1428
Total pages 10
Publisher ICIC International
Place of publication Kumamoto, Japan
Publication date 2009-05
ISSN 1349-4198
Keyword(s) Attribute combinations
Coverage
Concept learning
Summary Coverage is the range that covers only positive samples in attribute (or feature) space. Finding coverage is the kernel problem in induction algorithms because of the fact that coverage can be used as rules to describe positive samples. To reflect the characteristic of training samples, it is desirable that the large coverage that cover more positive samples. However, it is difficult to find large coverage, because the attribute space is usually very high dimensionality. Many heuristic methods such as ID3, AQ and CN2 have been proposed to find large coverage. A robust algorithm also has been proposed to find the largest coverage, but the complexities of time and space are costly when the dimensionality becomes high. To overcome this drawback, this paper proposes an algorithm that adopts incremental feature combinations to effectively find the largest coverage. In this algorithm, the irrelevant coverage can be pruned away at early stages because potentially large coverage can be found earlier. Experiments show that the space and time needed to find the largest coverage has been significantly reduced.
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Language eng
Field of Research 080201 Analysis of Algorithms and Complexity
Socio Economic Objective 890299 Computer Software and Services not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2009
Copyright notice ©2009, ICIC International
Persistent URL http://hdl.handle.net/10536/DRO/DU:30028681

Document type: Journal Article
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.