Combining one-class classifiers for robust novelty detection in gene expression data

Eduardo J. SpinosaAndré C. P. L. F. de Carvalho

One-class classification techniques are able to, based only on examples of a normal profile, induce a classifier that is capable of identifying novel classes or profile changes. However, the performance of different novelty detection approaches may depend on the domain considered. This paper applies combined one-class classifiers to detect novelty in gene expression data. Results indicate that the robustness of the classification is increased with this combined approach.

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