A Comparison of Ensemble Methods for Microarray Data Analysis
نویسنده
چکیده
Machine Learning tools are increasingly being applied to analyze data from microarray experiments. These include ensemble methods where weighted votes of constructed base classifiers are used to classify data. We compare the performance of AdaBoost, bagging and BagBoost on gene expression data from the yeast cell cycle. AdaBoost was found to be more effective for the data than bagging. BagBoost offered an advantage over AdaBoost due to the combination of the benefits of both bagging and boosting.
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