Selecting target concept in one-class classification for handling class imbalance problem

Microarray data classification is a difficult problem for computational techniques due to its inherent properties mainly, its imbalanced distribution and small sample size. Machine learning has been widely employed for handling this type of data predominantly applying two-class classification techniques. However, one-class approach has the ability to deal with imbalanced distribution and unexpected noise in the data. To deal with these situations it is considered that the best option is using the minority class as the target concept. This is reinforced by the idea of obtaining a classifier able to adjust itself to the specificity of the given class despite sacrificing the additional information about the second class. Although this consideration appears in different research, there are no thorough studies that prove it experimentally. In this paper, we investigate the suitability of employing minority class as the concept target in one-class classification to handle the class imbalance problem. A study over several microarray data sets is included. The results confirm that the use of minority class allows us to obtain better performance in one-class classification.