Abstract
Diseased sample classification is a very important application of microarray gene expression data. For sample classification the main problem is high dimensionality of genes (features). Among those huge numbers of genes only a small number of genes carry disease related information. To improve sample classification accuracy gene dimension reduction by selecting informative and non-redundant genes is a necessary task and for this purpose different feature selection methodologies are applied. In this regard, here, a comparative study of different measures to select informative and non-redundant genes is carried out. The effectiveness of different measures is assessed based on classification accuracy of different classifiers by applying them on different microarray gene expression datasets.
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Das, C., Bose, S., Banerjee, A., Dutta, S., Ghosh, K., Chattopadhyay, M. (2020). Comparative Performance Analysis of Different Measures to Select Disease Related Informative Genes from Microarray Gene Expression Data. In: Dawn, S., Balas, V., Esposito, A., Gope, S. (eds) Intelligent Techniques and Applications in Science and Technology. ICIMSAT 2019. Learning and Analytics in Intelligent Systems, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-42363-6_105
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DOI: https://doi.org/10.1007/978-3-030-42363-6_105
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