QIAN Wenbin, SHU Wenhao, YANG Bingru, et al., “An Incremental Algorithm to Feature Selection in Decision Systems with the Variation of Feature Set,” Chinese Journal of Electronics, vol. 24, no. 1, pp. 128-133, 2015,
Citation: QIAN Wenbin, SHU Wenhao, YANG Bingru, et al., “An Incremental Algorithm to Feature Selection in Decision Systems with the Variation of Feature Set,” Chinese Journal of Electronics, vol. 24, no. 1, pp. 128-133, 2015,

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

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
  • 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.
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