Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/114333
Title: GA based optimal feature extraction method for functional data classification
Authors: Wan, J.
Chen, Z. 
Chen, Y.
Bai, Z. 
Keywords: Classification
Feature extraction
Functional data
Genetic algorithm
Wavelet
Issue Date: Feb-2010
Source: Wan, J.,Chen, Z.,Chen, Y.,Bai, Z. (2010-02). GA based optimal feature extraction method for functional data classification. World Academy of Science, Engineering and Technology 62 : 909-915. ScholarBank@NUS Repository.
Abstract: Classification is an interesting problem in functional data analysis (FDA), because many science and application problems end up with classification problems, such as recognition, prediction, control, decision making, management, etc. As the high dimension and high correlation in functional data (FD), it is a key problem to extract features from FD whereas keeping its global characters, which relates to the classification efficiency and precision to heavens. In this paper, a novel automatic method which combined Genetic Algorithm (GA) and classification algorithm to extract classification features is proposed. In this method, the optimal features and classification model are approached via evolutional study step by step. It is proved by theory analysis and experiment test that this method has advantages in improving classification efficiency, precision and robustness whereas using less features and the dimension of extracted classification features can be controlled.
Source Title: World Academy of Science, Engineering and Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/114333
ISSN: 2010376X
Appears in Collections:Staff Publications

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