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研究生： 魏小乙
Wei, Hsiao-Yi
論文名稱： 以演化式運算法學習貝式網路
Learning Bayesian Network with Evolutionary Computing
指導教授： 李昇暾
Li, Sheng-Tun
學位類別： 碩士
Master
系所名稱： 管理學院 - 資訊管理研究所
Institute of Information Management
論文出版年： 2010
畢業學年度： 98
語文別： 英文
論文頁數： 38
中文關鍵詞： 貝式網路 、粒子群最佳化 、最小描述長度
外文關鍵詞： Bayesian networks, particle swarm optimization, minimum description length
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    • 從資料學習貝式網路是一件困難的問題。本研究提出一個新穎的方法，是結合粒子群最佳化及最小描述長度去學習貝式網路的結構。粒子群最佳化適合處理最佳化的問題，而最小描述長度是例子群最佳化的適用性函數。它可用來評估架構的優異程度並能夠在簡單化和正確率中取得平衡，在合理的時間內從複雜的模型中找出最佳的解決方法。此外，本研究也提出一個驗證的演算法，可去偵查出那些違反貝式網路標準的網路架構並加以修復。學習完架構後，各節點的條件機率可以從資料中計算出來。最後，實驗採用三種不同的資料去測試網路的效能及效率。

    Learning Bayesian networks from data is a difficult problem. This study presented a novel approach which combines particle swarm optimization, a competitive algorithm suitable for optimization problem, and minimum description length to learn the structure of Bayesian network. MDL was the fitness function in this learning algorithm to evaluate the goodness of the network. By adopting MDL, the balance between simplicity and accuracy was assured, which enabled the optimal solution for complex models to be found in reasonable time. A validation algorithm was proposed to inspect and repair networks which violated the standards of the Bayesian network. Conditional probabilities were then statistically derived from data to complete the quantitative part of Bayesian network. In the end, three realistic data sets were used to test the efficiency and effectiveness of this learned network.

    Abstract I 摘要 II 誌謝 III Table of contents IV List of tables VI List of figures VII Chapter 1. Introduction 1 1.1 Background 1 1.2 Research motivation 1 1.3 Research objectives 2 1.4 Structure of research 2 Chapter 2. Literature review 4 2.1 Bayesian networks 4 2.1.1 Issues about constructing Bayesian network 5 2.2 Particle swarm optimization 6 2.2.1 Parameters 8 2.2.2 Inertia weight 8 2.2.3 Constriction factors 9 2.3 Minimum description length 10 2.3.1 Applying the MDL principle to Bayesian networks 10 2.4 Related works 11 Chapter 3. Research method 14 3.1 Structure learning 14 3.1.1 Position representation 15 3.1.2 Velocity update 16 3.2 Fitness function 17 3.2.1 Encoding the network structure 17 3.2.2 Encoding the data given the model 18 3.3 Learning algorithm 18 3.3.1 Validation algorithm 19 3.3.2 Probability calculation 22 Chapter 4. Experiments and analysis 24 4.1 The data sets 24 4.2 PSO parameters setting 26 4.3 Results 26 4.3.1 Model performance 27 4.3.2 Parameter of PSO 28 4.3.3 Comparisons with other techniques 29 Chapter 5. Conclusion 33 5.1 Restrictive conditions 33 5.2 Discussions 34 5.3 Future research 34 References 36

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