Risk estimation for hierarchical classifier
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Risk function estimation for subproblems in a hierarchical classifier
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Application of hierarchical classifier to minimal synchronizing word problem
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
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This paper describes in full detail a model of a hierarchical classifier (HC). The original classification problem is broken down into several subproblems and a weak classifier is built for each of them. Subproblems consist of examples from a subset of the whole set of output classes. It is essential for this classification framework that the generated subproblems would overlap, i.e. some individual classes could belong to more than one subproblem. This approach allows to reduce the overall risk. Individual classifiers built for the subproblems are weak, i.e. their accuracy is only a little better than the accuracy of a random classifier. The notion of weakness for a multiclass model is extended in this paper. It is more intuitive than approaches proposed so far. In the HC model described, after a single node is trained, its problem is split into several subproblems using a clustering algorithm. It is responsible for selecting classes similarly classified. The main scope of this paper is focused on finding the most appropriate clustering method. Some algorithms are defined and compared. Finally, we compare a whole HC with other machine learning approaches.