An approach to feature selection for continuous features of objects

Hong-Wei, Wang, Guo-He, Li and Xue, Li (2016) An approach to feature selection for continuous features of objects. International Journal of Multimedia and Ubiquitous Engineering, 11 4: 67-78. doi:10.14257/ijmue.2016.11.4.08

Author Hong-Wei, Wang
Guo-He, Li
Xue, Li
Title An approach to feature selection for continuous features of objects
Journal name International Journal of Multimedia and Ubiquitous Engineering   Check publisher's open access policy
ISSN 1975-0080
Publication date 2016-01-01
Year available 2016
Sub-type Article (original research)
DOI 10.14257/ijmue.2016.11.4.08
Open Access Status DOI
Volume 11
Issue 4
Start page 67
End page 78
Total pages 12
Place of publication Daedoek-Gu, Daejon, Korea, Republic of
Publisher Science and Engineering Research Support Society
Language eng
Abstract A novel approach to feature selection is proposed for data space defined over continuous features. This approach can obtain a subset of features, such that the subset features can discriminate class labels of objects and the discriminant ability is prior or equivalent to that of the original features, so to effectively improve the learning performance and intelligibility of the classification model. According to the spatial distribution of objects and their classification labels, a data space is partitioned into subspaces, each with a clear edge and a single classification label. Then these labelled subspaces are projected to each continuous feature. The measurement of each feature is estimated for a subspace against all other subspace-projected features by means of statistical significance. Through the construction of a matrix of the measurements of the subspaces by all features, the subspace-projected features are ranked in a descending order based on the discriminant ability of each feature in the matrix. After evaluating a gain function of the discriminant ability defined by the best-so-far feature subset, the resulting feature subset can be incrementally determined. Our comprehensive experiments on the UCI Repository data sets have demonstrated that the approach of the subspace-based feature ranking and feature selection has greatly improved the effectiveness and efficiency of classifications on continuous features.
Keyword Continuous features
Data reduction
Feature ranking
Feature selection
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
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School of Information Technology and Electrical Engineering Publications
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