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Ambiguous Nodes in Networked Data based on Measuring Reliable Neighboring Probabilities

Advisor : Prof. Sing Ling Lee Student : Chao Chih Wang Date : 2013.01.04. Ambiguous Nodes in Networked Data based on Measuring Reliable Neighboring Probabilities. Outline. Introduction Network data Traditional VS Networked data Classification Collective Classification ICA

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Ambiguous Nodes in Networked Data based on Measuring Reliable Neighboring Probabilities

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  1. Advisor : Prof. Sing Ling Lee Student : Chao Chih Wang Date : 2013.01.04 Ambiguous Nodes in Networked Data based on Measuring Reliable Neighboring Probabilities

  2. Outline • Introduction • Network data • TraditionalVS Networked data Classification • Collective Classification • ICA • Problem • Our Method • Collective Inference With Ambiguous Node (CIAN) • Experiments • Conclusion

  3. Introduction – Network data • traditional data: • instances are independent of each other • network data: • instances are maybe related to each other • application: • emails • web page • paper citation independent related

  4. Introduction – Network data

  5. Introduction • traditionalVS network data classification 1 Class: 1 2 A A 1 E E D D 2 B : Class 1 F F B B 2 G G C C H H

  6. Introduction – Collective Classification • To classify interrelated instances using content features and link features. D D: E: 1 A B 1 1 0 0 1 1 0 2 1 1 0 2 0 0 2 + C E 1/2 1/2 0 1 0 0 1 0 0 1 We use : 1/2 1/2 0 2 1

  7. Introduction – ICA • ICA : Iterative Classification Algorithm Initial : Training local classifier use content features to predict unlabel instances Iterative{ for predict each unlabeled instance { set unlabeled instance ’s link feature use local classifier to predict unlabeled instance } } step1 step2

  8. Introduction – ICA Example unlabel data: Training data: training data: Class : 1 2 3 3 1 H C 2 3 A 3 1 B 2 1 2 2/3 0 1/3 1 A: 3 1/3 1/3 1/3 D E 2 I 3 1 F 1 1 G 2 1/2 1/2 0 B: 2 1/4 1/2 1/4

  9. Problem – AmbiguousNode C A • label the wrong class • judge the label with difficulty • make a mistake B G D 2 E F 2 1 1 1 1 or 2 ? 2 1

  10. Problem – use ICA Training data True class : unlabel data: B A C 2 2 1 training data: 1 A G 1 1 1 C F 1 2 D J 2 • Ambiguous B 1 1 I 2 1 2/3 1/3 0 A: 1 2 E 1 2/3 1/30 1 H C:

  11. Idea • Make a new prediction for neighbors of unlabeled instance • Use probability to compute link feature • Retrain the CC classifier

  12. Our Method –Method #1 • compute link feature • use probability General method : Class 1 : 1/3 Class 2 : 1/3 Class 3 : 1/3 A 1 3 2 ( 1 , 80%) ( 3 , 70%) Our method: Class 1 : 80/(80+60+70) Class 2 : 60/(80+60+70) Class 3 : 70/(80+60+70) ( 2, 60%)

  13. Our Method –Method #2 True class : B A To predict unlabeled instance ’s neighbors again. C 2 2 1 A A G G 1 1 1 1 C C 1 1 F F D D ( 1 , 70%) ( 1 , 70%) 2 2 ( 1 , 70%) ( 2, 80%) ( 1 , 70%) ( 2, 80%) 1 1 ( 2 , 80%) ( 2 , 80%) • Noise • Ambiguous B B ( 1 , 70%) ( 1 , 70%) 2 2 E E ( 2, 90%) ( 2, 60%) predict again predict again 2 2 1 1 1 1 H H B is ambiguous node. B is noise node.

  14. Our Method –Method #2 • To predictunlabeled instance ’s neighbors again • first iteration needs to predict again • difference between originaland predict label : • This iterationdoesn’t to adopt • Next iterationneed to predict again • similarity between originaland predict label : • Average the probability • Next iterationdoesn’t need to predict again A Example: new prediction ( 2, 60%) ( 2, 80%) 1 2 1 C B ( 2, 70%) ( 2, 60%) ( 2, 60%) ( 1 , 80%)

  15. Our Method –Method #2 x’sTrue label : 2 2 w • Ambiguous x z 1 3 y ( 1 , 60%) ( 3 , 60%) ( 2 , 70%) new prediction ( 2 , 70%) ( 3 , 60%) ( 2 , 80%) x is ambiguous (ornoise) node: Method B >Method C > Method A ( ? , ??%) ( 3 , 60%) ( 2 , 75%) x is notambiguous (ornoise) node: Method A >Method C > Method B x: Method A : (1 , 50%) Method B : (2 , 60%) Method C : (1 , 0%) not change class 2 Method A & Method B is too extreme. So we choose the Method C. change class not adopt

  16. Our Method –Method #2 Accuracy

  17. Our Method –Method#3 • Retrain CC classifier Initial ( ICA ) D A B 1 2 + E C retrain ( 3 , 70%) ( 1 , 80%) D 3 A ( 2 , 70%) 1 B 2 1 1 + 2 E C ( 2, 60%) ( 1 , 90%)

  18. CIAN Example – Ambiguous Training data True label: unlabel data: B A C training data: 2 2 1 B: 2 1 ( 1 , 60%) G ( 2 , 60%) ( 1 , 60%) A 2 1 1 predict again C ( 1 , 80%) 1 1 ( 2 , 80%) ( 2 , 60%) ( 1 , 80%) 2 ( 2 , 80%) D F 2 1 1/2 1/2 0 Our: • Ambiguous B 1 ( 1 , 70%) 2 ICA: E ( 2 , 80%)

  19. CIAN Example – Noise Training data True label: unlabel data: B A C training data: 2 2 1 B: 2 1 ( 1 , 70%) ( 1 , 60%) G ( 2 , 70%) A 2 1 1 predict again C ( 1 , 80%) 1 1 ( 2 , 80%) ( 2 , 80%) ( 1 , 80%) 2 ( 2 , 80%) D F 1 Our: • Noise B 1 2 ( 1 , 70%) 1/2 1/2 0 2 E ( 2 , 80%) ICA:

  20. CIAN • CIAN : Collective Inference With Ambiguous Node Initial : Training local classifier use content features to predict unlabel instances Iterative{ for predict each unlabel instance { for nb unlabeled instance ’s neighbors{ if(need to predict again) (class label, probability ) = local classifier(nb) } set unlabel instance ’s link feature (class label, probability ) = local classifier(A) } retrain local classifier } step1 step2 step3 step4 step5

  21. Experiments -Data sets

  22. Experiments-Experimental setting fixed argument ‧Compare with CO、ICA、CIAN

  23. Experiments • 1. misclassified nodes • Proportion of misclassified nodes (0%~30% , 80%) • 2. ambiguous nodes • NB vs SVM • 3. misclassified and Ambiguous nodes • Proportion of misclassified and ambiguous nodes (0%~30% , 80%) • 4.iteration & stable • number of iterations

  24. Experiments – 1. misclassified • Cora

  25. Experiments – 1. misclassified • CiteSeer

  26. Experiments – 1. misclassified • WebKB-texas

  27. Experiments – 1. misclassified • WebKB-washington

  28. Experiments – 1. misclassified • 80% of misclassifiednodes

  29. Experiments – 2. ambiguous • Cora Max ambiguous nodes : 429 Max ambiguous nodes : 356

  30. Experiments – 2. ambiguous • CiteSeer Max ambiguous nodes : 590 Max ambiguous nodes : 365

  31. Experiments – 2. ambiguous • WebKB-texas Max ambiguous nodes : 52 Max ambiguous nodes : 20

  32. Experiments – 2. ambiguous • WebKB-washington Max ambiguous nodes : 33 Max ambiguous nodes : 31

  33. Experiments – 2. ambiguous ‧ How much the same ambiguous nodes between NB and SVM?

  34. Experiments – 3. misclassified and ambiguous • Cora

  35. Experiments – 3. misclassified and ambiguous • CiteSeer

  36. Experiments – 3. misclassified and ambiguous • WebKB-texas

  37. Experiments – 3. misclassified and ambiguous • WebKB-washington

  38. Experiments – 3. misclassified and ambiguous • 80% of misclassified and ambiguousnodes

  39. Experiments ‧ When the accuracy of ICA is lower than CO?

  40. Experiments – 4. iteration & stable • Cora

  41. Experiments – 4. iteration & stable • CiteSeer

  42. Experiments – 4. iteration & stable • WebKB-texas

  43. Experiments – 4. iteration & stable • WebKB-washington

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