Bayesian network classifiers for probability-based metrics

Abstract

A large number of distance metrics have been proposed to measure the difference of two instances. Among these metrics, Short and Fukunaga metric (SFM) and minimum risk metric (MRM) are two probability-based metrics which are widely used to find reasonable distance between each pair of instances with nominal attributes only. For simplicity, existing works use naive Bayesian (NB) classifiers to estimate class membership probabilities in SFM and MRM. However, it has been proved that the ability of NB classifiers to class probability estimation is poor. In order to scale up the classification performance of NB classifiers, many augmented NB classifiers are proposed. In this paper, we study the class probability estimation performance of these augmented NB classifiers and then use them to estimate the class membership probabilities in SFM and MRM. The experimental results based on a large number of University of California, Irvine (UCI) data-sets show that using these augmented NB classifiers to estimate the class membership probabilities in SFM and MRM can significantly enhance their generalisation ability.

Keywords::

• Bayesian network classifiers
• probability-based metrics
• class probability estimation
• classification
• data mining

Acknowledgements

We thank the anonymous reviewers for their very useful comments and suggestions. This work was partially supported by the National Natural Science Foundation of China (Nos 61203287 and 61071188), the Provincial Natural Science Foundation of Hubei (No. 2011CDA103) and the Fundamental Research Funds for the Central Universities (No. CUG110405).