  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 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
five microarray datasets, and results demonstrate the effectiveness and
robustness of our method in comparison with six state-of-art ECOC methods. In
short, our work confirm the probability of applying DC to ECOC framework.
