In general, the analysis of microarray data requires two steps: feature selection and classification. From a variety of feature selection methods and classifiers, it is difficult to find optimal ensembles composed of any feature-classifier pairs. This paper proposes a novel method based on the evolutionary algorithm (EA) to form sophisticated ensembles of features and classifiers that can be used to obtain high classification performance. In spite of the exponential number of possible ensembles of individual feature-classifier pairs, an EA can produce the best ensemble in a reasonable amount of time. The chromosome is encoded with real values to decide the weight for each feature-classifier pair in an ensemble. Experimental results with two well-known microarray datasets in terms of time and classification rate indicate that the proposed method produces ensembles that are superior to individual classifiers, as well as other ensembles optimized by random and greedy strategies.
Bibliographical noteFunding Information:
Acknowledgement. The authors thank M. Symko-Davies, B. McConnell, K. Emery, S. Kurtz, J. Kiehl, T. Moriarty, W. Metzger, R. Ahrenkiel, B. Keyes, M. Romero, D. Friedman and J. Olson at NREL; and G. Kinsey, H. Cotal, A. Paredes, Y. Aguirre, P. Colter, T. Isshiki, M. Haddad, K. Barbour, M. Takahashi, M. Kalachian, and G. Glenn, and the entire multijunction solar cell team at Spec-trolab. This work was supported in part by the Department of Energy through the NREL High-Performance PV program (NAT-1-30620-01), and by Spectrolab.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computational Theory and Mathematics