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Many works have attempted to characterize the complexity of classification problems by measures extracted from their learning datasets. These indexes provide indicatives of the inherent difficulty in solving a given classification problem. Although regression problems are equally frequent, there is a lack of studies in Machine Learning dedicated to understanding their complexity. This paper proposes...
Gene marker selection from gene expression profiles has been extensively investigated in the Bioinformatics literature. The aim is usually to find a compact set of genes potentially correlated to a particular disease, which can then be candidate targets for new drugs and treatments. Available gene expression data sets are often noisy and sparse, having a low number of patient samples, for which a...
Machine Learning algorithms have been widely used for gene expression data classification, despite the fact that these data have often intrinsic limitations, such as high dimensionality and a small number of examples. Few studies try to characterize to which extent these aspects can influence the performance of the classification models induced. In this paper we compute different measures characterizing...
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