Abstract
Gene-expression microarray is a novel technology that allows to examine tens of thousands of genes at a time. For this reason, manual observation is not feasible anymore and machine learning methods are progressing to analyze these new data. Specifically, since the number of genes is very high, feature selection methods have proven valuable to deal with this unbalanced – high dimensionality and low cardinality – datasets. Our method is composed by a discretizer, a filter and the FVQIT (Frontier Vector Quantization using Information Theory) classifier. It is employed to classify eight DNA gene-expression microarray datasets of different kinds of cancer. A comparative study with other classifiers such as Support Vector Machine (SVM), C4.5, naïve Bayes and k-Nearest Neighbor is performed. Our approach shows excellent results outperforming all other classifiers.
This work was supported in part by Xunta de Galicia under Project Code 08TIC012105PR and under the program “Axudas para a consolidación e a estruturación de unidades de investigación competitivas” (code 2007/134), and by Spanish Ministerio de Ciencia e Innovación under Project Code TIN2009-10748. These last two are partially supported by the European Union ERDF.
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Porto-Díaz, I., Bolón-Canedo, V., Alonso-Betanzos, A., Fontenla-Romero, Ó. (2010). Local Modeling Classifier for Microarray Gene-Expression Data. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_2
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DOI: https://doi.org/10.1007/978-3-642-15825-4_2
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