Feature Selection with Data Field

YUAN Hanning; WANG Shuliang; LI Ying; FAN Jinghua

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Article Navigation > Chinese Journal of Electronics > 2014 > 23(4): 661-665

YUAN Hanning, WANG Shuliang, LI Ying, et al., “Feature Selection with Data Field,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 661-665, 2014,

Citation:

YUAN Hanning, WANG Shuliang, LI Ying, et al., “Feature Selection with Data Field,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 661-665, 2014,

YUAN Hanning, WANG Shuliang, LI Ying, et al., “Feature Selection with Data Field,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 661-665, 2014,

Citation:

YUAN Hanning, WANG Shuliang, LI Ying, et al., “Feature Selection with Data Field,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 661-665, 2014,

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Feature Selection with Data Field

1.
School of Software, Beijing Institute of Technology, Beijing 100081, China;
2.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430072, China;
3.
International School of Software, Wuhan University, Wuhan 430072, China

Funds: This paper is supported by the the National Natural Science Foundation of China (No.61173061, No.71201120), and the Doctoral Fund of Higher Education (No.20121101110036).

Received Date: 2013-07-01
Rev Recd Date: 2013-09-01
Publish Date: 2014-10-05

Abstract

Abstract

A new feature selection method is proposed for high-dimensional data clustering on the basis of data field. With the potential entropy to evaluate the importance of feature subsets, features are filtered by removing unimportant features or noises from the original datasets. Experiments show that the proposed method can sharply reduce the number of dimensions and effectively improve the clustering performance on WDBC dataset.
- Potential entropy,
- Data field,
- Feature selection,
- Dimension reduction,
- High-dimensional objects

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References(7)

References

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