Comparing Case-Based Bayesian Network and Recursive Bayesian Multi-net Classifiers

Recent work in Bayesian classifiers has shown that a better and more flexible representation of domain knowledge results in more accurate classifiers. We have recently examined a new type of Bayesian classifiers called Case-Based Bayesian Network (CBBN) classifiers. The basic idea is to partition the training data into semantically sound clusters. A local BN classifier is then learned independently from each cluster. Such a flexible organization of domain knowledge can represent dependency assertions among attributes more accurately and more relevantly than possible in traditional Bayesian classifiers (i.e., BN and BMN classifiers), hence improving classification accuracy. RBMNs also provide a more flexible representation scheme than BNs and generalize BMNs. Briefly, a RBMN is a Decision Tree (DT) with component BNs at the leaves. In this paper, we further explore our CBBN classifiers by comparing them to RBMN classifiers. RBMNs partition the data using a DT induction algorithm. By contrast, CBBNs rely on a flexible strategy for clustering that handles outliers, therefore, allowing more freedom to search for the best way to cluster the data and represent the knowledge. Our experimental results show that CBBN classifiers perform significantly better than RBMN classifiers