Classification with cluster-based Bayesian multi-nets using Ant Colony Optimisation

Bayesian Multi-net (BMN) classifiers consist of several local models, one for each data subset, to model asymmetric, more consistent dependency relationships among variables in each subset. This paper extends an earlier work of ours and proposes several contributions to the field of clustering-based BMN classifiers, using Ant Colony Optimization (ACO). First, we introduce a new medoidbased method for ACO-based clustering in the Ant-ClustBMB algorithm to learn BMNs. Both this algorithm and our previously introduced Ant-ClustBIB for instance-based clustering have their effectiveness empirically compared in the context of the “cluster-then-learn” approach, in which the ACO clustering step completes before learning the local BN classifiers. Second, we propose a novel “cluster-with-learn” approach, in which the ACO meta-heuristic performs the clustering and the BMN learning in a synergistic fashion. Third, we adopt the latter approach in two new ACO algorithms: ACO-ClustBIB , using the instance-based method, and ACO-ClustBMB , using the medoid-based method. Empirical results are obtained on 30 UCI datasets.