Publication Type : Conference Proceedings
Publisher : 2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017
Source : 2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017, Institute of Electrical and Electronics Engineers Inc. (2017)
ISBN : 9781509045594
Keywords : Classification (of information), Classification rates, Emerging applications, Extreme learning machine, Feature extraction, gene expression, Gene Expression Data, Genes, Genetic algorithms, Knowledge acquisition, Learning algorithms, Learning systems, Microarray data sets, Microarray gene expression data, Microarrays, Nearest Neighbor classifier, Training and testing
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Verified : Yes
Year : 2017
Abstract : Cancer diagnosis is one of the emerging applications in microarray gene expression data. Feature selection plays a crucial role because of the huge dimensionality and less training and testing samples. Finding a small subset of significant genes from a large set of gene expression data is a challenging task. This paper presents the usage of genetic algorithm as a tool to determine the informative gene subset and uses Extreme Learning Machines classifier to determine the classifier accuracy. Experiments are carried out on two microarray datasets and the results reveal that the proposed approach produces better classification rate compared to Support Vector Machines and nearest neighbor classifier.
Cite this Research Publication : A. Chinnaswamy, Sooraj, M., and Ramakrishnan, S., “Finding Expressed Genes using Genetic Algorithm and Extreme Learning Machines”, 2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017. Institute of Electrical and Electronics Engineers Inc., 2017.