Abstract
The One-Class Classification (OCC) approach is based on the assumption that samples are available only from a target class in the training phase. OCC methods have been applied with success to problems where the classes are very different in size. As class-imbalance problems are typical in protein classification tasks, we were interested in testing one-class classification algorithms for the detection of distant similarities in protein sequences and structures. We found that the OCC approach brought about a small improvement in classification performance compared to binary classifiers (SVM, ANN, Random Forest). More importantly, there is a substantial (50 to 100 fold) improvement in the training time. OCCs may provide an especially useful alternative for processing those protein groups where discriminative classifiers cannot be easily trained.
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Bánhalmi, A., Busa-Fekete, R., Kégl, B. (2009). A One-Class Classification Approach for Protein Sequences and Structures. In: Măndoiu, I., Narasimhan, G., Zhang, Y. (eds) Bioinformatics Research and Applications. ISBRA 2009. Lecture Notes in Computer Science(), vol 5542. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01551-9_30
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DOI: https://doi.org/10.1007/978-3-642-01551-9_30
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