Name
Title | ||
|---|---|---|
A comparative study of PCA, ICA and class-conditional ICA for Na ïve Bayes classifier |
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-73007-1_3 | IWANN |
Keywords | Field | DocType |
sample size,comparative study,independent component analysis,class-conditional ica,preprocessing procedure,appropriate preprocessing procedure,bayes classifier,large enough,principle component analysis,classification,naive bayes classifier,bayesian network | Pattern recognition,Naive Bayes classifier,Computer science,Bayesian network,Preprocessor,Artificial intelligence,Independent component analysis,Bayes error rate,Sample size determination,Principal component analysis,Machine learning,Bayes classifier | Conference |
Volume | ISSN | Citations |
4507 | 0302-9743 | 3 |
PageRank | References | Authors |
0.43 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li-Wei Fan | 1 | 71 | 5.36 |
| Kim Leng Poh | 2 | 62 | 7.36 |