Setting attribute weights for κ-NN based binary classification via quadratic programming

摘要

The κ-Nearest Neighbour (κ-NN) method is a typical lazy learning paradigm for solving classification problems. Although this method was originally proposed as a non-parameterised method, attribute weight setting has been commonly adopted to deal with irrelevant attributes. In this paper, we propose a new attribute weight setting method for κ-NN based classifiers using quadratic programming, which is particularly 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. To evaluate our method, we carried out a series of experiments on six established data sets. Experiments show that our method is quite practical for various problems and can achieve a stable increase in accuracy over the standard κ-NN method as well as a competitive performance. Another merit of the method is that it can use small training sets.