Extraction of Belief Knowledge from a Relational Database for Quantitative Bayesian Network Inference

Algorithm 4

Input: Training set 𝑇₁, testing set 𝑇₂, and attribute set 𝑋=𝑋₁,…,𝑋_𝑛.

Output: Restrictive TAN model.

Step 1. In the drafting phase, the TAN model is used as the basic structure.

Step 2. Mine association rules from data set 𝑇₁+𝑇₂, and transform these rules into FDs.

Thereafter, obtain the closure of FDs.

Step 3. In the thinning phase, remove redundant attributes and corresponding edges that

originate from these attributes. Thereafter, obtain the simplified structure.

Step 4. Learn the TAN model as the mapping structure with the rest of the attributes.

Step 5. In the thickening phase, compare the mapping structure and simplified structure.

If a new edge exists in the mapping structure, add the edge to the simplified structure.