A sequential feature extraction approach for naive bayes classification of microarray data

作者：Fan Liwei^*; Poh Kim Leng; Zhou Peng

来源：Expert Systems with Applications, 2009, 36(6): 9919-9923.

DOI：10.1016/j.eswa.2009.01.075

• Microarray data
• Naive Bayes
• Feature extraction
• Independent component analysis (ICA)
• Stepwise regression

Accurate classification of microarray data plays a vital role in cancer prediction and diagnosis. Previous Studies have demonstrated the usefulness of naive Bayes classifier in solving various classification problems. In microarray data analysis, however, the conditional independence assumption embedded in the classifier itself and the characteristics of microarray data, e.g. the extremely high dimensionality, may severely affect the classification performance of naive Bayes classifier. This paper presents a sequential feature extraction approach for naive Bayes classification of microarray data. The proposed approach consists of feature selection by stepwise regression and feature transformation by class-conditional independent component analysis. Experimental results on five microarray datasets demonstrate the effectiveness of the proposed approach in improving the performance of naive Bayes classifier in microarray data analysis.

• 出版日期2009-8
• 单位南京航空航天大学