ECAI-2002 Conference Paper

[full paper]

An Attribute Weight Setting Method for k-NN Based Binary Classification using Quadratic Programming

Lu Zhang, Frans Coenen, Paul Leng

In this paper, we propose a new attribute weight setting method for k-NN based classifiers using quadratic programming, which is particular suitable for binary classification problems. Our method formalises the attribute weight setting problem as a quadratic programming problem and exploits commercial software to calculate attribute weights. Experiments show that our method is quite practical for various problems and can achieve a competitive performance. Another merit of the method is that it can use small training sets.

Keywords: Machine Learning, Data Mining and Knowledge Discovery

Citation: Lu Zhang, Frans Coenen, Paul Leng: An Attribute Weight Setting Method for k-NN Based Binary Classification using Quadratic Programming. In F. van Harmelen (ed.): ECAI2002, Proceedings of the 15th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2002, pp.325-329.