Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/114333
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dc.titleGA based optimal feature extraction method for functional data classification
dc.contributor.authorWan, J.
dc.contributor.authorChen, Z.
dc.contributor.authorChen, Y.
dc.contributor.authorBai, Z.
dc.date.accessioned2014-12-02T06:52:48Z
dc.date.available2014-12-02T06:52:48Z
dc.date.issued2010-02
dc.identifier.citationWan, 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.
dc.identifier.issn2010376X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/114333
dc.description.abstractClassification 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.
dc.sourceScopus
dc.subjectClassification
dc.subjectFeature extraction
dc.subjectFunctional data
dc.subjectGenetic algorithm
dc.subjectWavelet
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.sourcetitleWorld Academy of Science, Engineering and Technology
dc.description.volume62
dc.description.page909-915
dc.identifier.isiutNOT_IN_WOS
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