Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Machine learning in DNA microarray analysis for cancer classification
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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DNA microarray technology has demonstrated to be an effective methodology for the diagnosis of cancers by means of microarray data classification. Although much research has been conducted during the recent years to apply machine learning techniques for microarray data classification, there are two important issues that prevent the use of conventional machine learning techniques, namely the limited availability of training samples and the existence of various uncertainties (e.g. biological variability and experiment variability). This paper presents a new ensemble machine learning approach to address these issues in order to achieve a robust microarray data classification. Ensemble learning combines a set of base classifiers as a committee to make appropriate decisions when classifying new data instances. In order to enhance the performance of the ensemble learning process, the approach presented includes a procedure to select optimal ensemble members that maximize the behavioural diversity. The proposed approach has been verified by three microarray datasets for cancer diagnosis. Experimental results have demonstrated that the classifier constructed by the proposed method outperforms not only the classifiers generated by the conventional machine learning techniques, but also the classifiers generated by two widely-used conventional Bagging and Boosting ensemble learning methods.