Name
Abstract | ||
|---|---|---|
Accurate classification of microarray data is very important for medical decision making. Past studies have shown that class-conditional independent component analysis (CC-ICA) is capable of improving the performance of naive Bayes classifier in microarray data analysis. However, when a microarray dataset has a small number of samples for some classes, the application of CC-ICA may become infeasible. This paper extends CC-ICA and proposes a partition-conditional independent component analysis (PC-ICA) method for naive Bayes classification of microarray data. Compared to ICA and CC-ICA, PC-ICA represents an in-between concept for feature extraction. Our experimental results on two microarray datasets show that PC-ICA is more effective than ICA in improving the performance of naive Bayes classification of microarray data. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.eswa.2010.05.068 | Expert Syst. Appl. |
Keywords | Field | DocType |
partition-conditional ica,feature extraction,naive bayes classification,microarray data analysis,mutual information,naïve bayes,microarray data,independent component analysis,microarray datasets,bayesian classification,partition-conditional independent component analysis,naive bayes classifier,microarray dataset,accurate classification,class-conditional independent component analysis,bayes classifier,naive bayes,conditional independence | Data mining,Computer science,Microarray analysis techniques,Artificial intelligence,Medical decision making,Pattern recognition,Naive Bayes classifier,Feature extraction,Mutual information,Independent component analysis,Partition (number theory),Machine learning,Bayes classifier | Journal |
Volume | Issue | ISSN |
37 | 12 | Expert Systems With Applications |
Citations | PageRank | References |
4 | 0.43 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li-Wei Fan | 1 | 71 | 5.36 |
| Kim-leng Poh | 2 | 279 | 30.30 |
| P. Zhou | 3 | 372 | 26.80 |