Combining One-Class Classifiers for Robust Novelty Detection in Gene Expression Data

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. IV BSB 12 Favor ver os Anais do Simpósio em Springer Verlag, Lecture Notes in Bioinformatics (LNBI número 3594) para este trabalho. Please see the Symposium Proceedings in Lecture Notes in Bioinformatics (LNBI nr. 3594), Springer Verlag, for this paper.