Exploring Case-Based Bayesian Networks and Bayesian Multi-nets for Classification

Recent work in Bayesian classifiers has shown that a better and more flexible representation of domain knowledge results in better classification accuracy. In previous work [1], we have introduced a new type of Bayesian classifier called Case-Based Bayesian Network (CBBN) classifiers. We have shown that CBBNs can capture finer levels of semantics than possible in traditional Bayesian Networks (BNs). Consequently, our empirical comparisons showed that CBBN classifiers have considerably improved classification accuracy over traditional BN classifiers. The basic idea behind our CBBN classifiers is to intelligently partition the training data into semantically sound clusters. A local BN classifier can then be learned from each cluster separately. Bayesian Multi-net (BMN) classifiers also try to improve classification accuracy through a simple partitioning of the data by classes. In this paper, we compare our CBBN classifiers to BMN classifiers. Our experimental results show that CBBN classifiers considerably outperform BMN classifiers.