Abstract
Multilabel classification is an important problem in bioinformatics and Machine Learning. In a conventional classification problem, examples belong to just one among many classes. When an example can simultaneously belong to more than one class, the classification problem is named multilabel classification problem. Protein function classification is a typical example of multilabel classification, since a protein may have more than one function. This paper describes the main characteristics of some multilabel classification methods and applies five methods to protein classification problems. For an experimental comparison of these methods, traditional machine learning techniques are used. The paper also compares different evaluation metrics used in multilabel problems.
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Cerri, R., da Silva, R.R.O., de Carvalho, A.C.P.L.F. (2009). Comparing Methods for Multilabel Classification of Proteins Using Machine Learning Techniques. In: Guimarães, K.S., Panchenko, A., Przytycka, T.M. (eds) Advances in Bioinformatics and Computational Biology. BSB 2009. Lecture Notes in Computer Science(), vol 5676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03223-3_10
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DOI: https://doi.org/10.1007/978-3-642-03223-3_10
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