Classifier fusion to predict breast cancer tumors based on microarray gene expression data

Authors:
Mansoor Raza;Iqbal Gondal;David Green;Ross L. Coppel
Affiliations:
Faculty of IT, GSCIT;Faculty of IT, GSCIT;Faculty of IT, GSCIT;,Deparment of Microbiology
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Year:
2005

Citing 2
Cited 1

A decision-theoretic generalization of on-line learning and an application to boosting

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Editorial: Information fusion in the realm of medical applications - A bibliographic glimpse at its growing appeal

Information Fusion

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Abstract

Classifiers are often data dependent as they perform better on one type of data, but fail to perform well for another data set. There is a need for robust classification algorithms which exhibit performance stability for multiple types of data. This problem can be addressed if different classifiers are fused to identify a particular class. In this paper, we have implemented the idea of classifier fusion using six different classifiers to classify the microarray gene expression data of breast cancer patients. The paper uses two classifier fusion models: majority voting and random bagging to improve the accuracy of the classifiers. Our experimental results have shown that the new proposed classifiers fusion methodology have outperforms single classification models.