Ensemble classifiers based on correlation analysis for DNA microarray classification

Since accurate classification of DNA microarray is a very important issue for the treatment of cancer, 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. In spite of the many advantages of mutually error-correlated ensemble classifiers, they are limited in performance. It is difficult to create an optimal ensemble for DNA analysis that deals with few samples with large features. Usually, different feature sets are provided to learn the components of the ensemble expecting the improvement of classifiers. If the feature sets provide similar information, the combination of the classifiers trained from them cannot improve the performance because they will make the same error and there is no possibility of compensation. In this paper, we adopt correlation analysis of feature selection methods as a guideline of the separation of features to learn the components of ensemble. We propose ...

• Type: Journal
• Year: 2006
• Where: IJON
• Authors: Kyung-Joong Kim, Sung-Bae Cho
• Keywords: Ensemble Classifiers, Error-correlated Ensemble Classifiers, IJON 2006, Various Expert Classifiers