Open AccessAn Incremental Algorithm to Feature Selection in Decision Systems with the Variation of Feature Set
Author(s) -
Qian Wenbin,
Shu Wenhao,
Yang Bingru,
Zhang Changsheng
Publication year - 2015
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2015.01.021
Subject(s) - feature selection , feature (linguistics) , dependency (uml) , computer science , algorithm , variation (astronomy) , set (abstract data type) , artificial intelligence , function (biology) , selection (genetic algorithm) , pattern recognition (psychology) , minimum redundancy feature selection , data mining , machine learning , philosophy , linguistics , physics , astrophysics , programming language , evolutionary biology , biology
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.