Bayesian Multi-nets (BMNs) are a special kind of Bayesian network (BN) classifiers that consist of several local networks, typically, one for each predictable class, to model an asymmetric set of variable dependencies given each class value. Alternatively, multi-nets can be learnt upon arbitrary partitions of a dataset, in which each partition holds more consistent variable dependencies given the data subset in the partition. This paper proposes two contributions to the approach that clusters the dataset into separate data subsets to build asymmetric local BN classifiers, one for each subset. First, we extend the K-modes algorithm, previously used by the Case-Based Bayesian Network Classifiers (CBBN) approach to create clusters before learning the BN classifiers. Second, we introduce the Ant-Clust-B algorithm that employs Ant Colony Optimization (ACO) to learn clustering-based BMNs. Ant-Clust-B uses ACO in the clustering step before learning the local BN classifiers. Empirical results are obtained from experiments on 18 UCI datasets.
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