Abstract
Microarray datasets are a challenge for classical computational techniques because of the large dimensionality of their feature space front to a reduced number of samples, besides they usually present unbalanced classes. Thanks to this unbalanced situation, in a previous research, the superiority of one-class classification for handling microarray datasets was proved. This paper presents a new study that tries to improve the behavior of the traditional techniques, specifically Support Vector Machines, by considering oversampling techniques. The experimental results achieved demonstrate that despite inclusion of these methods the performance of classical classifiers still remains below one-class approach.
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Acknowledgments
This work has been supported in part by the Secretaría de Estado de Investigación of the Spanish Government (Grant TIN2015-65069-C2-1-R), and by the Xunta de Galicia (Grant GRC2014/035) with the European Union FEDER funds.
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Pérez-Sánchez, B., Fontenla-Romero, O., Sánchez-Maroño, N. (2016). Two-Class with Oversampling Versus One-Class Classification for Microarray Datasets. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_47
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