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Rough sets and Laplacian score based cost-sensitive feature selection

Yu, Shenglong and Zhao, Hong 2018, Rough sets and Laplacian score based cost-sensitive feature selection, PLoS ONE, vol. 13, no. 6, pp. 1-23, doi: 10.1371/journal.pone.0197564.

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Title Rough sets and Laplacian score based cost-sensitive feature selection
Author(s) Yu, ShenglongORCID iD for Yu, Shenglong orcid.org/0000-0002-2983-7344
Zhao, Hong
Journal name PLoS ONE
Volume number 13
Issue number 6
Article ID e0197564
Start page 1
End page 23
Total pages 23
Publisher PLOS
Place of publication San Francisco, CA
Publication date 2018-06-18
ISSN 1932-6203
1932-6203
Keyword(s) Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
ATTRIBUTE REDUCTION
CLASSIFICATION
ACQUISITION
KNOWLEDGE
MODEL
Summary Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of “good” features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.
Language eng
DOI 10.1371/journal.pone.0197564
Indigenous content off
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2018, Yu, Zhao
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30146639

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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.