ECAI 2004 Conference Paper

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On Multiclass Active Learning with Support Vector Machines

Klaus Brinker

In supervised machine learning, a training set of examples which are assigned to the correct target labels is a necessary prerequisite. However, in many applications, the task of assigning class labels cannot be conducted in an automatic manner, but involves human decisions and is therefore time-consuming and expensive. In the case of classification learning, the active learning framework has been considered to address this problem. While most research on active learning in the field of kernel machines has focused on binary problems, less attention has been given to the problem of learning classifiers in the case of multiple classes. We consider three common decomposition methods to state multiclass problems in terms of sets of binary classification problems and propose novel active learning heuristics to reduce the labeling effort. Various experiments conducted on real-world datasets demonstrate the merits of our approach in comparison to previous research.

Keywords: Active Learning, Support Vector Machine, Multiclass Classification

Citation: Klaus Brinker: On Multiclass Active Learning with Support Vector Machines. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.969-970.


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ECAI-2004 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the Universitat Politècnica de València on behalf of Asociación Española de Inteligencia Artificial (AEPIA) and Associació Catalana d'Intel-ligència Artificial (ACIA).