Exploring Supervised Learning and Factorization in Bayesian Networks with Hugin
This project focuses on supervised learning and factorization in Bayesian Networks (BNs) using Hugin software. Participants will create a BN with six nodes and binary variables, generate synthetic cases, and apply structure and parameter learning algorithms, including the EM algorithm. The project involves executing various learning algorithms on datasets derived from the "Asia.xbl" network, comparing outcomes, and assessing classification models like Naive Bayes. A thorough examination of the Hamming distance between original and learned network structures will also be conducted.
Exploring Supervised Learning and Factorization in Bayesian Networks with Hugin
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Presentation Transcript
PROJECTS ON SUPERVISED AND FACTORIZATION BAYESIAN NETWORKS Concha Bielza, Pedro Larrañaga Universidad Politécnica de Madrid Course 2007/2008
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)
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
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
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
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
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)
