MBNC: The Experiment Platform for Bayesian Classifiers Based on Matlab
Cheng Ze-kai · 2004
To test and evaluate the performance of Bayesian Classifier, it is absolutely necessary to carry through contrastive experiment using different data sets. Current packages for Bayesian Classifier experiment are designed for certain purposes, so that it can't satisfy the needs of different research. It introduces the building of experiment platform MBNC for Bayesian Classifiers using Matlab based on BNT, including the system structure and the main function of MBNC, the Classifiers built on MBNC: the Nave Bayesian Classifier NBC, the Tree Augmented Nave Bayesian Classifier TANC based on Mutual Information and Conditional Mutual Information, Bayesian Network Classifier BNC based on K2 and GS algorithm. MBNC is tested by standard data set from UCI and the results show that the performance of Bayesian classifiers built on MBNC are preceded similar works and the quantity of programming much less than that using current packages, which indicates that the platform works correctly, effectively and stably. Now the experiments for optimizing Bayesian Classifiers and the study of dealing with missing data are carried through on MBNC.