Abstract
The selection of relevant genes in microarray is an important task, since that in a single experiment expressions of thousands of genes are extracted. One way to evaluate feature selection methods in a dataset is by clustering the instances that have similar behaviors. The aim of this paper is to use a set of indexes that measure the quality of a clustering and, through the multiobjective optimization of this set, to show how it is possible to find the best feature selection methods in genes expression datasets obtained by microarray technique.
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Garcia, R., Paraiso, E.C., Nievola, J.C. (2011). Multiobjective Optimization of Indexes Obtained by Clustering for Feature Selection Methods Evaluation in Genes Expression Microarrays. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_42
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DOI: https://doi.org/10.1007/978-3-642-23878-9_42
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