Abstract
Microarray data classification is a critical challenge for computational techniques due to its inherent characteristics, mainly small sample size and high dimension of the input space. For this type of data two-class classification techniques have been widely applied while one-class learning is considered as a promising approach. In this paper, we study the suitability of employing the one-class classification for microarray datasets while the role played by feature selection is analyzed. The superiority of this approach is demonstrated by comparison with the classical approach, with two classes, on different benchmark data sets.
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Pérez-Sánchez, B., Fontenla-Romero, O., Sánchez-Maroño, N. (2015). One-Class Classification for Microarray Datasets with Feature Selection. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2015. Communications in Computer and Information Science, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-319-23983-5_30
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DOI: https://doi.org/10.1007/978-3-319-23983-5_30
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