We present a method, called equivalence learning, which applies a two-class classification approach to object-pairs defined within a multi-class scenario. The underlying idea is that instead of classifying objects into their respective classes, we classify object pairs either as equivalent (belonging to the same class) or non-equivalent (belonging to different classes). The method is based on a vectorisation of the similarity between the objects and the application of a machine learning algorithm (SVM, ANN, LogReg, Random Forests) to learn the differences between equivalent and non-equivalent object pairs, and define a unique kernel function that can be obtained via equivalence learning. Using a small dataset of archaeal, bacterial and eukaryotic 3-phosphoglycerate-kinase sequences we found that the classification performance of equivalence learning slightly exceeds those of several simple machine learning algorithms at the price of a minimal increase in time and space requirements. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Kertész-Farkas, A., Kocsor, A., & Pongor, S. (2007). Equivalence learning in protein classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4571 LNAI, pp. 824–837). Springer Verlag. https://doi.org/10.1007/978-3-540-73499-4_62
Mendeley helps you to discover research relevant for your work.