An Incremental Algorithm to Feature Selection in Decision Systems with the Variation of Feature Set. Issue 1 (1st January 2015)

• Record Type: Journal Article
• Authors: Qian, Wenbin; Shu, Wenhao; Yang, Bingru; Zhang, Changsheng
• Is Part Of: Chinese journal of electronics. Volume 24:Issue 1(2015)
• Page Start: 128
• Page End: 133
• Publication Date: 2015-01-01
• Subjects: decision theory -- feature selection -- learning (artificial intelligence) -- rough set theory; incremental algorithm -- feature selection -- decision system -- feature set variation -- dynamic decision system -- pattern recognition -- machine learning -- dependency function -- rough sets; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2015.01.021
• Languages: English
• ISSNs: 1022-4653

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.