ECAI 2004 Conference Paper

[full paper]

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.