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A novel ensemble of classifiers for microarray data classification
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DOI：10.1016/j.asoc.2008.01.006.png)
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Abstract
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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 is proposed. Firstly, in order to extract useful features and reduce dimensionality, different feature selection methods such as correlation analysis, Fisher-ratio is 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 with PSO (Particle Swarm Optimization) algorithm. At last, appropriate classifiers are selected to construct the classification committee using EDAs (Estimation of Distribution Algorithms). Experiments show that the proposed method produces the best recognition rates on four benchmark databases. (c) 2008 Elsevier B.V. All rights reserved.
Keywords:
microarray classification
estimation of distribution algorithms (EDA)
particle swarm optimization (PSO)
ensemble learning
correlation analysis
Fisher-ratio
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