  This paper introduces a novel parameter estimation method for the probability
tables of Bayesian network classifiers (BNCs), using hierarchical Dirichlet
processes (HDPs). The main result of this paper is to show that improved
parameter estimation allows BNCs to outperform leading learning methods such as
Random Forest for both 0-1 loss and RMSE, albeit just on categorical datasets.
  As data assets become larger, entering the hyped world of "big", efficient
accurate classification requires three main elements: (1) classifiers with
low-bias that can capture the fine-detail of large datasets (2) out-of-core
learners that can learn from data without having to hold it all in main memory
and (3) models that can classify new data very efficiently.
  The latest Bayesian network classifiers (BNCs) satisfy these requirements.
Their bias can be controlled easily by increasing the number of parents of the
nodes in the graph. Their structure can be learned out of core with a limited
number of passes over the data. However, as the bias is made lower to
accurately model classification tasks, so is the accuracy of their parameters'
estimates, as each parameter is estimated from ever decreasing quantities of
data. In this paper, we introduce the use of Hierarchical Dirichlet Processes
for accurate BNC parameter estimation.
  We conduct an extensive set of experiments on 68 standard datasets and
demonstrate that our resulting classifiers perform very competitively with
Random Forest in terms of prediction, while keeping the out-of-core capability
and superior classification time.
