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Knowledge Transfer via Multiple Model Local Structure Mapping Jing Gao Wei Fan Jing JiangJiawei Han University of Illinois at Urbana-Champaign IBM T. J.

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Presentation on theme: "Knowledge Transfer via Multiple Model Local Structure Mapping Jing Gao Wei Fan Jing JiangJiawei Han University of Illinois at Urbana-Champaign IBM T. J."— Presentation transcript:

1 Knowledge Transfer via Multiple Model Local Structure Mapping Jing Gao Wei Fan Jing JiangJiawei Han University of Illinois at Urbana-Champaign IBM T. J. Watson Research Center

2 2/17 Standard Supervised Learning New York Times training (labeled) test (unlabeled) Classifier 85.5% New York Times

3 3/17 In Reality…… New York Times training (labeled) test (unlabeled) Classifier 64.1% New York Times Labeled data not available! Reuters

4 4/17 Domain Difference Performance Drop traintest NYT New York Times Classifier 85.5% Reuters NYT ReutersNew York Times Classifier 64.1% ideal setting realistic setting

5 5/17 Other Examples Spam filtering –Public collection personal inboxes Intrusion detection –Existing types of intrusions unknown types of intrusions Sentiment analysis –Expert review articles blog review articles The aim –To design learning methods that are aware of the training and test domain difference Transfer learning –Adapt the classifiers learnt from the source domain to the new domain

6 6/17 All Sources of Labeled Information training (labeled) test (completely unlabeled) Classifier New York Times Reuters Newsgroup …… ?

7 7/17 A Synthetic Example Training (have conflicting concepts) Test Partially overlapping

8 8/17 Goal Source Domain Target Domain Source Domain Source Domain To unify knowledge that are consistent with the test domain from multiple source domains

9 9/17 Summary of Contributions Transfer from multiple source domains –Target domain has no labeled examples Do not need to re-train –Rely on base models trained from each domain –The base models are not necessarily developed for transfer learning applications

10 10/17 Locally Weighted Ensemble C1C1 C2C2 CkCk …… Training set 1 Test example x Training set 2 Training set k …… X-feature value y-class label

11 11/17 Optimal Local Weights C1C1 C2C2 Test example x Higher Weight Optimal weights –Solution to a regression problem –Impossible to get since f is unknown!

12 12/17 Graph-based Heuristics Graph-based weights approximation –Map the structures of a model onto the structures of the test domain –Weight of a model is proportional to the similarity between its neighborhood graph and the clustering structure around x. Higher Weight

13 13/17 Experiments Setup Data Sets –Synthetic data sets –Spam filtering: public collection personal inboxes (u01, u02, u03) (ECML/PKDD 2006) –Text classification: same top-level classification problems with different sub-fields in the training and test sets (Newsgroup, Reuters) –Intrusion detection data: different types of intrusions in training and test sets. Baseline Methods –One source domain: single models (WNN, LR, SVM) –Multiple source domains: SVM on each of the domains –Merge all source domains into one: ALL –Simple averaging ensemble: SMA –Locally weighted ensemble: LWE

14 14/17 Experiments on Synthetic Data

15 15/17 Experiments on Real Data

16 16/17 Conclusions Locally weighted ensemble framework –transfer useful knowledge from multiple source domains Graph-based heuristics to compute weights –Make the framework practical and effective


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