A New ECOC Algorithm for Multiclass Microarray Data Classification

The classification of multi-class microarray datasets is a hard task because of the small samples size in each class and the heavy overlaps among classes. To effectively solve these problems, we propose a novel Error Correcting Output Code (ECOC) algorithm by Enhance Class Separability related Data Complexity measures during encoding process, named as ECOCECS. In this algorithm, two nearest neighbor related DC measures are deployed to extract the intrinsic overlapping information from microarray data. Our ECOC algorithm aims to search an optimal class split scheme by minimizing these measures. The class splitting process ends when each class is separated from others, and then the class assignment scheme is mapped as a coding matrix. Experiments are carried out on seven microarray datasets, and results demonstrate the effectiveness and robustness of our method in comparison with four state-of-art ECOC methods. In short, our work shows that it is promising to apply the DC theory to ECOC framework.