ECAI 2004 Conference Paper

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Piece-Wise Model Fitting Using Local Data Patterns

Ricardo Vilalta, Muralikrishna Achari, Christoph Eick

In this paper we propose a novel classification algorithm that fits models of different complexity on separate regions of the input space. The goal is to achieve a balance between global and local learning strategies by decomposing the classification task into simpler subproblems; each task narrows the learning problem to a local region of high example density over the input space. Specifically, our proposed approach is to apply a clustering algorithm to all training examples that are class-uniform; each cluster becomes an intermediate concept that is learned by selecting a model with an (estimated) optimal degree of complexity. Experimental results on real-world domains show consistent good performance in predictive accuracy with our piece-wise model fitting strategy.

Keywords: classification, model fitting, clustering, local learning, global learning

Citation: Ricardo Vilalta, Muralikrishna Achari, Christoph Eick: Piece-Wise Model Fitting Using Local Data Patterns. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.559-563.

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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).