Cancer Prediction Using Diversity-Based Ensemble Genetic Programming

Abstract

Abstract. Combining a set of classifiers has often been exploited to improve the classification performance. Accurate as well as diverse base classifiers are prerequisite to construct a good ensemble classifier. Therefore, estimating diversity among classifiers has been widely investigated. This paper presents an ensemble approach that combines a set of diverse rules obtained by genetic programming. Genetic programming generates interpretable classification rules, and diversity among them is directly estimated. Finally, several diverse rules are combined by a fusion method to generate a final decision. The proposed method has been applied to cancer classification using gene expression profiles, which is one of the important issues in bioinformatics. Experiments on several popular cancer datasets have demonstrated the usability of the method. High performance of the proposed method has been obtained, and the accuracy has increased by diversity among the base classification rules.

Using genetic programming to classify node positive patients in bladder cancer

“…Importantly, the process does not require a large amount of the prior knowledge or effort in terms of structure selection or dimensionality reduction. There have been several initiatives where GP is used for analyzing medical/biological data [11,12] and for discrimination of cancers [13,14].…”