An Incremental Algorithm to Feature Selection in Decision Systems with the Variation of Feature Set

QIAN Wenbin; SHU Wenhao; YANG Bingru; ZHANG Changsheng

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Article Navigation > Chinese Journal of Electronics > 2015 > 24(1): 128-133

QIAN Wenbin, SHU Wenhao, YANG Bingru, ZHANG Changsheng. An Incremental Algorithm to Feature Selection in Decision Systems with the Variation of Feature Set[J]. Chinese Journal of Electronics, 2015, 24(1): 128-133.

Citation:

QIAN Wenbin, SHU Wenhao, YANG Bingru, ZHANG Changsheng. An Incremental Algorithm to Feature Selection in Decision Systems with the Variation of Feature Set[J]. Chinese Journal of Electronics, 2015, 24(1): 128-133.

Citation:

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An Incremental Algorithm to Feature Selection in Decision Systems with the Variation of Feature Set

1.
School of Software, Jiangxi Agriculture University, Nanchang 330045, China;
2.
Beijing Key Laboratory of Knowledge Engineering of Material Science, Beijing 100083, China;
3.
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China

Funds: This work is supported in part by the National Natural Science Foundation of China (No.61175048, No.71461013), the Key Project of Ministry of Science and Technology of China (No.2010IM020900), the 2012 Ladder Plan Project of Beijing Key Laboratory of Knowledge Engineering for Materials Science (No.Z121101002812005), and Zhejiang Provincial Natural Science Foundation of China (No.LY13F020024).

Received Date: 2013-05-01
Rev Recd Date: 2013-07-01
Publish Date: 2015-01-10

Abstract

Abstract

Feature selection is a challenging problem in pattern recognition and machine learning. In real-life applications, feature set in the decision systems may vary over time. There are few studies on feature selection with the variation of feature set. This paper focuses on this issue, an incremental feature selection algorithm in dynamic decision systems is developed based on dependency function. The incremental algorithm avoids some recomputations, rather than retrain the dynamic decision system as new one to compute the feature subset from scratch. We firstly employ an incremental manner to update the new dependency function, then we incorporate the calculated dependency function into the incremental feature selection algorithm. Compared with the direct (non-incremental) algorithm, the computational efficiency of the proposed algorithm is improved. The experimental results on different data sets from UCI show that the proposed algorithm is effective and efficient.
- Rough sets,
- Feature selection,
- Attribute reduction,
- Incremental algorithm,
- Dynamic data set

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References(21)

References

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