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Advances in Bayesian Learning
- 1. Machine Learning in Performance Management Irina Rish IBM T.J. Watson Research Center January 24, 2001
- 3. Pattern discovery, classification, diagnosis and prediction Learning problems: examples System event mining Events from hosts Time End-user transaction recognition Remote Procedure Calls (RPCs) BUY? SELL? OPEN_DB? SEARCH? Transaction1 Transaction2
- 8. Our approach: Classification + Segmentation (similar to text classification) (similar to speech understanding, image segmentation) Problem 2: both segment and label (EUT recognition) 1 2 1 3 4 1 2 3 1 2 1 3 2 3 1 2 1 2 3 1 2 1 2 4 Tx1 Tx3 Tx1 Tx3 Tx1 Tx3 Tx1 Tx3 Tx1 Tx3 Tx1 Tx3 Tx1 Tx3 Tx1 Tx3 Tx2 Tx2 Tx2 Tx2 Tx2 Tx2 Tx2 Tx2 Unsegmented RPC's Segmented RPC's and Labeled Tx's Tx2 Problem 1: label segmented data (classification) Labeled Tx's Segmented RPC's Tx3 Tx2 1 3 3 1 3 1 3 1 3 1 3 1 3 1 3 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 1 3 Tx1 Tx 1 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 Tx3 1 1 1 1 1 1 1 1
- 10. Classification scheme RPCs labeled with EUTs Learning Classifier Unlabeled RPCs EUTs Training phase Feature Extraction Classifier Training data: Operation phase “ Test” data: Feature Extraction Classification
- 13. Transaction recognition: segmentation + classification Naive Bayes classifier Dynamic programming (Viterbi search) (Recursive) DP equation:
- 22. ABLE: A gent B uilding and L earning E nvironment
- 24. How does it work?
- 27. Why Naïve Bayes does well? And when? When independence assumptions do not hurt classification? Class-conditional feature independence: Unrealistic assumption! But why/when it works? True NB estimate P(class|f) Class Intuition: wrong probability estimates wrong classification! Naïve Bayes: Bayes-optimal :
- 32. From Naïve Bayes to Bayesian Networks Naïve Bayes model: independent features given class Bayesian network (BN) model: Any joint probability distributions = P(S) P(C|S) P(B|S) P(X|C,S) P(D|C,B) P(S, C, B, X, D)= Query: P (lung cancer =yes | smoking =no, dyspnoea =yes ) = ? lung Cancer Smoking X-ray Bronchitis Dyspnoea P(D|C,B) P(B|S) P(S) P(X|C,S) P(C|S) CPD: C B D=0 D=1 0 0 0.1 0.9 0 1 0.7 0.3 1 0 0.8 0.2 1 1 0.9 0.1
- 33. Example: Printer Troubleshooting (Microsoft Windows 95) [Heckerman, 95] Print Output OK Correct Driver Uncorrupted Driver Correct Printer Path Net Cable Connected Net/Local Printing Printer On and Online Correct Local Port Correct Printer Selected Local Cable Connected Application Output OK Print Spooling On Correct Driver Settings Printer Memory Adequate Network Up Spooled Data OK GDI Data Input OK GDI Data Output OK Print Data OK PC to Printer Transport OK Printer Data OK Spool Process OK Net Path OK Local Path OK Paper Loaded Local Disk Space Adequate