1 / 18

Actively Transfer Domain Knowledge

Actively Transfer Domain Knowledge. Transfer when you can, otherwise ask and don’t stretch it. Xiaoxiao Shi † Wei Fan ‡ Jiangtao Ren † † Sun Yat-sen University ‡ IBM T. J. Watson Research Center. Standard Supervised Learning. training (labeled). test (unlabeled). Classifier.

eli
Download Presentation

Actively Transfer Domain Knowledge

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Actively Transfer Domain Knowledge Transfer when you can, otherwise ask and don’t stretch it Xiaoxiao Shi† Wei Fan‡ Jiangtao Ren† †Sun Yat-sen University ‡IBM T. J. Watson Research Center

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

  3. In Reality…… How to improve the performance? training (labeled) test (unlabeled) 47.3% Labeled data are insufficient! New York Times New York Times

  4. Solution I : Active Learning training (labeled) test (unlabeled) Classifier 83.4% New York Times New York Times $ Label LabelingCost Domain Expert

  5. Solution II : Transfer Learning Out-of-domain training (labeled) In-domain test (unlabeled) Transfer Classifier 82.6%?? 43.5% New York Times Reuters Significant Differences No guarantee transfer learning could help! Accuracy drops

  6. Motivation Both have disadvantages, what to choose? • Active Learning: • Labeling cost • Transfer Learning: • Domain difference risk

  7. Test Unlabeled in-domain Training Data Proposed Solution (AcTraK) Active Learner choose Classifier ? ? Reliable, label by the classifier Classification Result Transfer Classifier Decision Function Labeled Training Unreliable out-domain training (labeled) Label Domain Expert Reuters

  8. ML+ Train mapping Transfer Mo L+ Mo In-domain Label +/L+ +/L- L- ML- In-domain labeled (very few) -/L+ -/L- Train Transfer Classifier Train Train In-domain labeled (few) Train L+ = { (x,y=+/-)|Mo(x)=‘L+’ } the true in-domain label may be either‘-’ or ‘+’ P(+|X, ML+) ML+ + L+ Out-of-domain dataset (labeled) P(L+|X, Mo) Mo P(+|X, ML-) L- X: In-domain unlabeled ML- - P(L-|X, Mo) • Classify X by out-of-domain Mo: P(L+|X, Mo) and P(L-|X, Mo). • Classify X by mapping classifiers ML+ and ML-: P(+|X, ML+) and P(+|X, ML-). • Then the probability for X to be “+” is: • T(X) = P(+|X) = P(L+|X, Mo) × P(+|X, ML+) + P(L-|X, Mo) ×P(+|X, ML-)

  9. Our Solution (AcTraK) unlabeled Training Data Test Active Learner Classifier ? ? Reliable, label by the classifier Classification Result Transfer Classifier Decision Function Labeled Training Unreliable outdomain training (labeled) Label Domain Expert 9 Reuters

  10. Decision Function ? Transfer Classifier • In the following, ask the domain expert to label the instance, not the transfer classifier: when prediction by transfer classifier is unreliable, ask domain experts a) Conflict b) Low in confidence c) Few labeled in-domain examples

  11. Decision Function T(x): prediction by the transfer classifier ML(x): prediction given by the in-domain classifier b) Confidence? c) Size? a) Conflict? AcTraK asks the domain expert to label the instance with probability of Decision Function: Label by Transfer Classifier Label by Domain Expert R : random number [0,1]

  12. Properties • It can reduce domain difference risk.- According to Theorem 2, the expected error is bounded. • It can reduce Labeling cost. - According to Theorem 3, the query probability is bounded.

  13. Theorems Maximum size expected error of the transfer classifier

  14. Experiments setup • Data Sets • Synthetic data sets • Remote Sensing: data collected from regions with a specific ground surface condition  data collected from a new region • Text classification: same top-level classification problems with different sub-fields in the training and test sets (Newsgroup) • Comparable Models • Inductive Learning model: AdaBoost, SVM • Transfer Learning model: TrAdaBoost (ICML’07) • Active Learning model: ERS (ICML’01)

  15. Experiments on Synthetic Datasets In-domain: 2 labeled training & testing 4 out domain labeled training

  16. Experiments on Real World Dataset • Evaluation metric: • Compared with transfer learning on accuracy. • Compared with active learning onIEA (Integral Evaluation on Accuracy).

  17. 20 Newsgroup • comparison with active learner ERS 1. Comparison with Transfer Learner 2. Comparison with Active Learner

  18. Conclusions • Actively Transfer Domain Knowledge • Reduce domain difference risk: transfer useful knowledge (Theorem 2) • Reduce labeling cost: query domain experts only when necessary (Theorem 3)

More Related