The data-independent multi-branch tree hierarchical structure is proposed for the knowledge representation, because it has strong expressing ability and can be easily expanded.

If control and data were split into independent streams, control data could be dealt with in a more timely manner, resulting in better utilization of available resources.

Naive Bayes classifiers often break down when the size of the training examples per class are not balanced or when the data is not independent enough.