Comparative Analysis of the Impact of Discretization on the Classification with Naïve Bayes and Semi-Naïve Bayes Classifiers

While data could be discrete and continuous (defined as ordinal numerical features), some classifiers, like Naive Bayes (NB), work only with or may perform better with the discrete data. We focus on NB due to its popularity and linear training time. We investigate the impact of eight discretization algorithms (Equal Width, Equal Frequency, Maximum Entropy, IEM, CADD, CAIM, MODL, and CACC) on the classification with NB and two modern semi-NB classifiers, LBR and AODE.Our comprehensive empirical study indicates that unsupervised discretization algorithms are the fastest while among the supervised algorithms the fastest is maximum entropy, followed by CAIM and IEM. The CAIM and MODL discretizers generate the lowest and the highest number of discrete values, respectively.We compare the time to build the classification model and classification accuracy when using raw and discretized data. We show that discretization helps to improve the classification with the NB when compared with flexible NB which models continuous features using Gaussian kernels. The AODE classifier obtains on average the best accuracy, while the best performing setup includes discretization with IEM and classification with AODE. The runner-up setups include CAIM and CACC coupled with AODE and CAIM and IEM coupled with LBR. IEM and CAIM are shown to provide statistically significant improvements across all considered datasets for LBR and AODE classifiers when compared with using NB on the continuous data. We also show that the improved accuracy comes at the trade-off of substantially increased runtime.