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

2. Hugin Lite 6.7 1.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) Factorization Exercise

3. Hugin Lite 6.7 ------------------------------------------------------------------------ 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 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 Factorization Exercice

5. Weka 1.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 Factorization Exercice

6. BAYESIA Supervised Exercice 1.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 1.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) Supervised Exercice