Comparing Bayesian Network Classifiers
This work addresses the need for efficient and accurate classifiers in machine learning and data mining, though it is incremental as it builds on existing Bayesian network methods.
The paper empirically evaluates Bayesian network classifiers, finding that those learned using conditional-independence algorithms are competitive or superior to best-known classifiers, with relatively small computational time, and demonstrates a new algorithm for more effective learning.
In this paper, we empirically evaluate algorithms for learning four types of Bayesian network (BN) classifiers - Naive-Bayes, tree augmented Naive-Bayes, BN augmented Naive-Bayes and general BNs, where the latter two are learned using two variants of a conditional-independence (CI) based BN-learning algorithm. Experimental results show the obtained classifiers, learned using the CI based algorithms, are competitive with (or superior to) the best known classifiers, based on both Bayesian networks and other formalisms; and that the computational time for learning and using these classifiers is relatively small. Moreover, these results also suggest a way to learn yet more effective classifiers; we demonstrate empirically that this new algorithm does work as expected. Collectively, these results argue that BN classifiers deserve more attention in machine learning and data mining communities.