Please use this identifier to cite or link to this item:
|dc.title||GA based optimal feature extraction method for functional data classification|
|dc.identifier.citation||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.|
|dc.description.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.|
|dc.contributor.department||STATISTICS & APPLIED PROBABILITY|
|dc.description.sourcetitle||World Academy of Science, Engineering and Technology|
|Appears in Collections:||Staff Publications|
Show simple item record
Files in This Item:
There are no files associated with this item.
checked on Aug 16, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.