An Evolutionary Algorithm Approach to Optimal Ensemble Classifiers for DNA Microarray Data Analysis

• Published in: IEEE transactions on evolutionary computation Vol. 12; no. 3; pp. 377 - 388
• Main Authors: KIM, Kyung-Joong, CHO, Sung-Bae
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Applied sciences; Artificial intelligence; Benchmark testing; Biological and medical sciences; Classification; Computer science; Computer science; control theory; systems; Data analysis; DNA; DNA microarray; ensemble; evolutionary algorithm (EA); Evolutionary computation; Exact sciences and technology; feature selection; Fundamental and applied biological sciences. Psychology; Gene expression; Learning and adaptive systems; Molecular and cellular biology; Molecular genetics; Neoplasms; Principal component analysis; real-valued encoding; Robustness; Support vector machines; High performance; DNA computation; Probabilistic approach; Optimal algorithm; Evolutionary algorithm; DNA chip; evolutionary algorithm (EA); real-valued encoding; ensemble; Aggregate model; DNA microarray; Classification; Microprocessor; Bioinformatics; feature selection; Molecular computation
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2007.906660

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.