Untitled

Abstract

Abstract.We have found one reason why AdaBoost tends not to perform well on gene expression data, and identified simple modifications that improve its ability to find accurate class prediction rules. These modifications appear especially to be needed when there is a strong association between expression profiles and class designations. Cross-validation analysis of six microarray datasets with different characteristics suggests that, suitably modified, boosting provides competitive classification accuracy in general.Sometimes the goal in a microarray analysis is to find a class prediction rule that is not only accurate, but that depends on the level of expression of few genes. Because boosting makes an effort to find genes that are complementary sources of evidence of the correct classification of a tissue sample, it appears especially useful for such gene-efficient class prediction. This appears particularly to be true when there is a strong association between expression profiles and class designations, which is often the case for example when comparing tumor and normal samples.

Robustification of Naïve Bayes Classifier and Its Application for Microarray Gene Expression Data Analysis

“…Otherwise, it keeps almost equal performance. Then we investigated the performance of the proposed method in a comparison of some popular classifiers including Support Vector Machine (SVM), K -Nearest Neighbors ( K NN), and AdaBoost which are widely used for gene expression data analysis [27–29]. In that comparison, we used both simulated and real gene expression datasets.…”

“…We observed that the proposed β -NBC outperforms existing robust linear classifiers as mentioned earlier. Then we investigate the performance of the proposed method in a comparison with some popular classifiers including Support Vector Machine (SVM), k -nearest neighbors ( K NN), and AdaBoost; those are widely used in gene expression data analysis [27–29]. We observed that the proposed method improves the performance over the others in presence of outliers.…”