Enhancing Boosting by Feature Non-Replacement for Microarray Data Analysis

Guile, G. and Wang, W. (2007) Enhancing Boosting by Feature Non-Replacement for Microarray Data Analysis. In: International Joint Conference on Neural Networks, 2007-08-12 - 2007-08-17.

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Abstract

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 cancer datasets.

Item Type: Conference or Workshop Item (Paper)
Faculty \ School: Faculty of Science > School of Computing Sciences

University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology
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Depositing User: Vishal Gautam
Date Deposited: 16 May 2011 17:30
Last Modified: 18 Mar 2020 07:51
URI: https://ueaeprints.uea.ac.uk/id/eprint/23447
DOI: 10.1109/IJCNN.2007.4370995

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