A Novel Ensemble Approach for Cancer Data Classification

Authors:
Yaou Zhao;Yuehui Chen;Xueqin Zhang
Affiliations:
School of Information Science and Engineering, University of Jinan, Jinan 250022, P.R. China;School of Information Science and Engineering, University of Jinan, Jinan 250022, P.R. China;School of Information Science and Engineering, University of Jinan, Jinan 250022, P.R. China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

Micorarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes and a few hundreds of samples. Such extreme asymmetry between the dimensionality of genes and samples presents several challenges to conventional clustering and classification methods. In this paper, a novel ensemble method based on correlation analysis is proposed. Firstly, in order to extract useful features and reduce dimensionality, different feature selection methods based on correlation analysis are used to form different feature subsets. Then a pool of candidate base classifiers is generated to learn the subsets which are re-sampling from the different feature subsets. At last, appropriate classifiers are selected to construct the classification committee using EDA (Estimation of Distribution Algorithms) algorithm. Experiments show that the proposed method produces the best recognition rates on two benchmark databases.