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Boosting techniques have been applied to DNA microarray data because their high dimensionality has made them difficult to analyze. However, classification performance varies between boosting algorithms. We have investigated factors affecting error in boosting ensemble classifiers on DNA Microarray data: number of training samples, number of boosting iterations, complexity of base learners and diversity...
We have investigated strategies for enhancing ensemble learning algorithms for the analysis of high-dimensional biological data. Specifically we investigated strategies to force classifiers to consider the possible interactions between features. As a result an algorithm that induces decision trees with a feature non-replacement mechanism has been devised and tested on DNA microarray and proteomic...
We have investigated the use of boosting techniques for feature selection for microarray data analysis. We propose a novel algorithm for feature selection and have tested it on three datasets. The results clearly show that our boosting technique for feature selection outperformed the Wilcoxon-Mann-Whitney U-test commonly used in microarray data analysis, and produced more accurate boosting ensembles...
We have investigated strategies for enhancing ensemble learning algorithms for DNA microarray data analysis. By using modified versions of AdaBoost, LogitBoost and BagBoosting we have shown that feature non-replacement provides an effective enhancement to the performance of all three algorithms, and overall, BagBoosting with feature non-replacement had the lowest error rates when used on six commonly-used...
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