Budgeted Machine Learning of Bayesian Networks

1. Budgeted Machine Learning ofBayesian Networks Michael R. GubbelsDr. Stephen D. Scott Department of Computer Science and EngineeringUniversity of Nebraska-Lincoln McNair Scholars ProgramAugust 2009

2. Overview • Introduction • Methods • Results • Discussion • Conclusions

3. Machine Learning The concern of machine learning is to design algorithms for learning general knowledge from a collection of related special cases called examples. A collection of related examples forms a data set for the concept relating its examples. Learning algorithms construct a general model for relationships among the characteristics or attributes in data set. Using this model, the attributes of interest or labels of an example can be learned in terms of its other attributes. This allows the label of a new example or instance to be predicted in terms of its attributes. 3

4. Machine Learning Learning Algorithm observed by generates LearnedModel presented to (Yes, No, Yes, Positive, ?) predicts Yes

5. Budgeted Machine Learning In budgeted machine learning, an algorithm is given a budget and must pay for the attributes observed during learning. Using the attributes purchased from examples, the algorithm constructs a general model for a concept. The labels of examples are free, so can be observed without penalty, but the attributes have an associated cost. Attributes are purchased until the budget is exhausted.

6. Budgeted Machine Learning Budgeted Learning Algorithm purchases from observed by evaluated by generates LearnedModel presented to (Yes, No, Yes, Positive, ?) predicts Yes

7. Budgeted Machine Learning • Attribute selection policies • Round robinPurchases one value at a time from each attributee.g., (A1, A2, A3, A4, A1, A2, A3, A4,A1,… ) • Biased robin Repeatedly purchases values from the same while those purchases improve the model e.g., ( A1, A1, A2, A3, A3, A3, A4, A1, A1, … )

8. Budgeted Machine Learning

9. Purpose • Evaluate how well existing algorithms learn Bayesian networks for use in classification • Produce more accurate probability estimates • Should improve efficacy of existing algorithms that depend on such estimates • Explicitly represent attribute independencies • Should facilitate learning of a more accurate model • Should improve classification performance of model

10. Methods • Generated data sets • Used Asia and ALARM Bayesian network models • Asia network has 8 attributes: • Predicted Bronchitis • ALARM network has 37 attributes • Predicted Breathing Pressure • Constructed model • Evaluated the learned networks

11. Methods • Generated data sets • Constructed model • Used round robin and biased robin policies • Learned naïve and complex Bayesian networks • Structures were given • Uniform and noisy prior knowledge • Uniform attribute cost • Varied the learning algorithm’s total budget • Evaluated the learned networks

12. Methods • Evaluated the learned networks • Constructed model • Evaluated the learned networks • For uniform and noisy prior knowledge • For many numbers of purchases • Baseline (“best possible”) classification performance of model

13. Results

14. Results

15. Discussion • Naïve Bayesian networks • Converge to baseline faster with uniform priors • Bayesian networks • Have more accurate baseline than naïve networks • Converge to baseline faster with noisy priors

16. Conclusions • Bayesian networks learned using existing algorithms converge to baseline • Bayesian networks may be preferable to naïve networks when learning complex concepts or when prior knowledge is available • Future work • Evaluate more existing policies with Bayesian networks • Analysis of models learned for complex concepts • New algorithms to exploit Bayesian network structure • Learning from data with different cost models

17. Acknowledgements • Dr. Stephen D. ScottResearch Mentor • Kun DengGraduate Student • Amy LehmanGraduate Student Mentor • UNL McNair Scholars Program

18. Bibliography Lizotte, D. J., Madani, O., & Greiner, R. (2003). Budgeted learning of naïve-Bayes classifiers. Uncertainty in Artificial Intelligence, 378-385. Tong, S., &Koller, D. (2001). Active learning for parameter estimation in Bayesian networks. International Joint Converences on Artificial Intelligence. Deng, K., Bourke, C., Scott, S., Sunderman, J., &Zheng, Y. (2007). Bandit-based algorithms for budgeted learning. Seventh IEEE International Conference on Data Mining, 463-468. Neapolitan, R. E. (2004). Learning bayesian networks. New Jersey: Pearson Prentice Hall. Mitchell, T. M. (1997). Machine learning. New York: McGraw-Hill.

19. Pseudocode Figure 4. Pseudocode for the round robin, biased robin, and random data selection policies. A is the set of attributes available to purchase values from, and a is a particular attribute in A. E is the set of examples, and e is a particular example in E. v is an attribute value in an example. M denotes the specific model being learned, and the variables mold and mnew represent the correctness of model M before and after learning new information.

20. Pseudocode