Ant colony algorithms for constructing Bayesian multi-net classifiers

Abstract

Bayesian Multi-nets (BMNs) are a special kind of Bayesian network (BN) classifiers that consist of several local Bayesian networks, one for each predictable class, to model an asymmetric set of variable dependencies given each class value. Deterministic methods using greedy local search are the most frequently used methods for learning the structure of BMNs based on optimizing a scoring function. Ant Colony Optimization (ACO) is a meta-heuristic global search method for solving combinatorial optimization problems, inspired by the behavior of real ant colonies. In this paper, we propose two novel ACO-based algorithms with two different approaches to build BMN classifiers: $\mathrm{ABC}\text{-}{\mathrm{Miner}}_{\mathit{l}}^{\mathit{m}\mathit{n}}$ and $\mathrm{ABC}\text{-}{\mathrm{Miner}}_{\mathit{g}}^{\mathit{m}\mathit{n}}$ . The former uses a local learning approach, in which the ACO algorithm completes the construction of one local BN at a time. The latter uses a global approach, which involves building a complete BMN classifier by each single ant in the colony. We experimentally evaluate the performance of our ant-based algorithms on 33 benchmark classification datasets, where our proposed algorithms are shown to be significantly better than other commonly used deterministic algorithms for learning various Bayesian classifiers in the literature, as well as competitive to other well-known classification algorithms.