Fast & Efficient Learning of Bayesian Networks from Data: Knowledge Discovery and Causality
This work addresses the computational bottleneck in Bayesian network learning for big data analytics, offering incremental improvements in efficiency.
The paper tackles the problem of learning Bayesian network structures from data by introducing two novel algorithms, FSBN and SSBN, which achieve up to 52% and 72% computation cost reductions, respectively, while matching the quality of existing methods.
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.