Enhancing Boosting by Feature Non-Replacement for Microarray Data Analysis

Geoffrey R. Guile; Wenjia Wang

doi:10.1109/IJCNN.2007.4370995

Enhancing Boosting by Feature Non-Replacement for Microarray Data Analysis

Geoffrey R. Guile, Wenjia Wang

Research output: Contribution to conference › Paper

5 Citations (Scopus)

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.

Original language	English
Pages	430-435
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2007.4370995
Publication status	Published - 2007
Event	2007 International Joint Conference on Neural Networks - Orlando, United States Duration: 12 Aug 2007 → 17 Aug 2007

Conference

Conference	2007 International Joint Conference on Neural Networks
Country/Territory	United States
City	Orlando
Period	12/08/07 → 17/08/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IJCNN.2007.4370995

Cite this

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title = "Enhancing Boosting by Feature Non-Replacement for Microarray Data Analysis",

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.",

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