Guile, Geoffrey R. and Wang, Wenjia (2007) Enhancing Boosting by Feature Non-Replacement for Microarray Data Analysis. In: 2007 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)
Uncontrolled Keywords:	sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Machine learning in computational biology (former - to 2018) Faculty of Science > Research Groups > Health Computing
Depositing User:	Vishal Gautam
Date Deposited:	16 May 2011 17:30
Last Modified:	15 Sep 2026 04:39
URI:	https://uea-test-2026.eprints-hosting.org/id/eprint/23447
DOI:	10.1109/IJCNN.2007.4370995

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