Abstract: Dissimilarity based ensemble of Extreme Learning Machine (D-ELM) gets stable classification results on gene expression data classification.While this algorithm is based on the classification accuracy,it cannot meet the requirement to get the minimum misclassification of cost-sensitive classification,when the misclassification costs are not equal.This paper used probability estimate and misclassification cost to reconstruct the classification results.Then we proposed the algorithm of Cost-sensitive Dissimilarity based ensemble of Extreme Learning Machine (CS-D-ELM).This algorithm is applied on the data of gene expression and the experiment demonstrates that it can get better result.

Key words: Extreme learning machine,Dissimilarity based ensemble,Cost-sensitive,Gene expression data,Classification