Abstract
Cancer is a genetic disease characterized by various types of changes in the activity of genes at cell level. The molecules produced by activity of genes are captured by the expression values of genes as obtained by genome sequencing technologies like microarray technology. This gene expression data can be computationally analyzed to detect abnormalities in one or more genes of an organism. Thus, diseases like cancer can be successfully diagnosed and classified into subtypes by analyzing gene expression datasets. Since this data is very high dimensional and very few genes are associated with incidence of a type of cancer. Therefore, many methods have been proposed for selecting subset of most relevant genes for classification task. In this paper, we have proposed a method for feature selection from cancer gene expression data based on binary particle swarm optimization and logistic regression. The ensemble consists of binary particle swarm optimization and logistic regression classifier. The proposed method was compared to existing feature selection-classification approaches and the results show the proposed method to perform better with respect to classification accuracy and learning rate.
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Acknowledgements
Authors would like to thank Department of Science and Technology (DST), Government of India, for supporting this research work under the grant DST-ICPS 2019.
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Koul, N., Manvi, S.S. (2021). Colon Cancer Classification Using Binary Particle Swarm Optimization and Logistic Regression. In: Tavares, J.M.R.S., Chakrabarti, S., Bhattacharya, A., Ghatak, S. (eds) Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 164. Springer, Singapore. https://doi.org/10.1007/978-981-15-9774-9_20
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DOI: https://doi.org/10.1007/978-981-15-9774-9_20
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