A cost-sensitive rotation forest algorithm for gene expression data classification

Existing works show that the rotation forest algorithm has competitive performance in terms of classification accuracy for gene expression data. However, most existing works only focus on the classification accuracy and neglect the classification costs. In this study, we propose a cost-sensitive rotation forest algorithm for gene expression data classification. Three classification costs, namely misclassification cost, test cost and rejection cost, are embedded into the rotation forest algorithm. This extension of the rotation forest algorithm is named as cost-sensitive rotation forest algorithm. Experimental results show that the cost-sensitive rotation forest algorithms effectively reduce the classification cost and make the classification result more reliable.