1.School of Computer and Information Technology, Shanxi University, Taiyuan 030006 2.Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006
Abstract Feature selection of functional data aims to choose those features slightly correlated and strongly representative, from the huge functional information. And it can simplify the calculation and improve the generalization ability. Traditional feature selection methods are directly applied in functional data, and the results are not effective or efficient. A functional data oriented fast feature selection(FFS) method integrating principal component analysis(PCA) and minimum convex hull is proposed in this paper. FFS can obtain stable subset of features fleetly. Considering the correlation embedding in features, the result of FFS can serve as initial feature subset of other iterative approaches. This means twice feature selection will be needed. As a popular feature selection method for functional data, conditional mutual information(CMI) is adopted. The experiment results on UCR datasets demonstrate the effectiveness of FFS, and a selection strategy under different demands of time cost or classification accuracy is given through the contrast experiments.
About author:: (MA Chen, born in 1991, master student. Her research interests include functional data analysis.) (WANG Wenjian(Corresponding author), born in 1968, Ph.D., professor. Her research interests include machine learning, computing intelligence and image processing.) (JIANG Gaoxia, born in 1987, Ph.D. candidate. His research interests include functional data analysis and machine learning.)
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2017, Vol. 30 
