Please use this identifier to cite or link to this item:
|Title:||A comparative study of PCA, ICA and class-conditional ICA for naïve bayes classifier||Authors:||Fan, L.
Independent component analysis
Naïve bayes classifier
Principle component analysis
|Issue Date:||2007||Citation:||Fan, L.,Poh, K.L. (2007). A comparative study of PCA, ICA and class-conditional ICA for naïve bayes classifier. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4507 LNCS : 16-22. ScholarBank@NUS Repository.||Abstract:||The performance of the Naïve Bayes classifier can be improved by appropriate preprocessing procedures. This paper presents a comparative study of three preprocessing procedures, namely Principle Component Analysis (PCA), Independent Component Analysis (ICA) and class-conditional ICA, for Naïve Bayes classifier. It is found that all the three procedures keep improving the performance of the Naïve Bayes classifier with the increase of the number of attributes. Although class-conditional ICA has been found to be superior to PCA and ICA in most cases, it may not be suitable for the case where the sample size for each class is not large enough. © Springer-Verlag Berlin Heidelberg 2007.||Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||URI:||http://scholarbank.nus.edu.sg/handle/10635/72232||ISBN:||9783540730064||ISSN:||03029743|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 29, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.