Fast & Efficient Learning of Bayesian Networks from Data: Knowledge
  Discovery and Causality
Minn Sein Fu Shunkai
Structure learning is essential for Bayesian networks (BNs) as it uncovers
causal relationships, and enables knowledge discovery, predictions, inferences,
and decision-making under uncertainty. Two novel algorithms, FSBN and SSBN,
based on the PC algorithm, employ local search strategy and conditional
independence tests to learn the causal network structure from data. They
incorporate d-separation to infer additional topology information, prioritize
conditioning sets, and terminate the search immediately and efficiently. FSBN
achieves up to 52% computation cost reduction, while SSBN surpasses it with a
remarkable 72% reduction for a 200-node network. SSBN demonstrates further
efficiency gains due to its intelligent strategy. Experimental studies show
that both algorithms match the induction quality of the PC algorithm while
significantly reducing computation costs. This enables them to offer
interpretability and adaptability while reducing the computational burden,
making them valuable for various applications in big data analytics.