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The conventional Multi-dimensional Bayesian Network Classifier (MBNC) requires its structure be bi-partitie. Removing this constraint can result into a new tool named General MBNC (GMBNC), and it enables us to model the underlying joint distribution more correctly. Based on iterative local search of Markov blankets, an algorithm called IPC-GMBNC was proposed to induce the exact structure of GMBNC. The proposed algorithm has good scalability because it does not need to recover the global Bayesian Network (BN) first. The experiments on samples generated from known Bayesian network structures indicate that IPC-GMBNC is effective, and it brings great reduction on computing complexity compared to global search approach, eg PC algorithm.

摘要:

传统多维贝叶斯网络分类器 (MBNC) 限制其模型结构必须是二分的, 通过移除该限制可得到更准确的对关联分布建模的通用 MBNC (GMBNC). 基于局部马尔可夫毯的 …