A Robust Model for Gene Anlysis and Classification

Abstract

The development of microarray gene technology has provided a large volume of data to many fields. Microarray data analysis and classification has demonstrated an effective methodology for the effective diagnosis of diseases and cancers. Although much research has been performed on applying machine learning techniques for microarray data classification during the past years, it has been shown that conventional machine learning techniques have intrinsic drawbacks in achieving accurate and robust classifications. So it is more desirable to make a decision by combining the results of various expert classifiers rather than by depending on the result of only one classifier. We address the microarray dataset based cancer classification using a newly proposed ensemble classifier generation technique referred to as RotBoost, which is constructed by combining Rotation Forest and AdaBoost. The experiments conducted with 8 microarray datasets, among which a classification tree is adopted as the base learning algorithm, demonstrate that RotBoost can generate ensemble classifiers with significantly lower prediction error than either Rotation Forest or AdaBoost more often than the reverse.