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PROJECTS ON SUPERVISED AND FACTORIZATION BAYESIAN NETWORKS

PROJECTS ON SUPERVISED AND FACTORIZATION BAYESIAN NETWORKS. Concha Bielza , Pedro Larrañaga Universidad Politécnica de Madrid. Course 2007/2008. Hugin Lite 6.7. Factorization Exercise. Build a Bayesian network of your invention with six nodes and binary variables

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PROJECTS ON SUPERVISED AND FACTORIZATION BAYESIAN NETWORKS

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  1. PROJECTS ON SUPERVISED AND FACTORIZATION BAYESIAN NETWORKS Concha Bielza, Pedro Larrañaga Universidad Politécnica de Madrid Course 2007/2008

  2. Hugin Lite6.7 FactorizationExercise • Build a Bayesian network of your invention with six nodes and binary variables • How to Build BNs (Hugin GUI Help) • 2. Generate 50, 100, 200 and 400 cases from the previously built Bayesian network • Case Generator (Hugin GUI Help) • 3. Structure learning with PC and NPC algorithms with two level of significance (0.05 and 0.10) • Structure Learning (Hugin GUI Help) • 4. Parameter learning with the EM learning algorithm • EM learning (Hugin GUI Help)

  3. Hugin Lite6.7 ------------------------------------------------------------------------ PC NPC ------------------------------------------------------------------------ 0.05 Simulation 50 0.10 ------------------------------------------------------------------------ 0.05 Simulation 100 0.10 ----------------------------------------------------------------------- 0.05 Simulation 50 0.10 ------------------------------------------------------------------------ 0.05 Simulation 100 0.10 ---------------------------------------------------------------------- Hamming distance between the structure of the original Bayesian network, and the one obtained after learning

  4. BAYESIA Factorization Exercice • Generate two data bases (50 and 500 instances • and different percentage of missing data) • from the “Asia.xbl” Bayesian network • 2. Apply the following learning algoithms: • “EQ”, “SopLEQ”, “Tabo” and “TaboOrder” • to both data bases • 3. Compare the induced Bayesian networks with the • “Asia.xbl” • 4. Obtain information in Internet about the learning • algorithms

  5. Weka Factorization Exercice • Using the “tips-discrete-cfs9.arff” dataset • 2. Learn Bayesian network structures with: • - Conditional independence tests • - Local search • - Global search • 3. Estimate the parameters: • - Simple estimation • - BMA estimator

  6. BAYESIA Supervised Exercice • Generate 3 files (100, 200 and 400 cases) from the • “Asia.xbl” Bayesian network • 2. Choose variable “Cancer” as the class (target) variable • 3. Induce the following classifiers: • Naive Bayes • Augmented naive Bayes • Markov blanket • 4. Compare the accuracies of the different models in the • 3 files

  7. Weka Supervised Exercice • Open the file “tips-discrete-cfs9.arff” • 2. Learn naive Bayes and TAN models • 3. Obtain the corresponding accuracies • with a 10-fold cv validation method • 4. Repeat the exercice with a FSS method • (Select Attributes in Weka)

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