The document discusses a novel approach to label propagation in the context of semi-supervised learning, specifically through a method called amendable clamping (LPAC). This method aims to improve accuracy in multi-label classification by addressing the issues of missing labels, showing a 45% higher performance compared to traditional approaches. The study utilizes the Siam 2007 text mining competition dataset and proposes future work to enhance label correlation utilization and address datasets with incorrect or missing labels.

1. Label Propagation Using Amendable Clamping
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○ Tatsurou Miyazaki
Tokyo University of Science
Yasunobu Sumikawa
Tokyo University of Science

2. Motivation
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3. Motivation
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4. Motivation
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5. Motivation
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6. Motivation
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7. Motivation
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8. Motivation
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Suicide bombings is not assigned.

9. Motivation
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• Mass murder and Suicide bombings is not assigned.

10. Motivation
• Problem 1 : taking high cost
• Semi-supervised learning is known as better approach.
• Ex.) Lapel Propagation (LP)
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11. 11
Labeled data
Unlabeled data
0.81
0.07
0.62
Motivation

12. Motivation
• Problem 2 : the quality of dataset fall.
• Wrong : Linear Neighborhood Propagation (LNP)
• Missing : our proposed approach
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13. Effect of missing labels
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The number of missing labels
Themicro-averagedF-scores• The missing of labels is a serious issue for accuracy.

14. Effect of missing labels
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The number of missing labels
Themicro-averagedF-scores• The missing of labels is a serious issue for accuracy.

15. Effect of missing labels
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The number of missing labels
Themicro-averagedF-scores• The missing of labels is a serious issue for accuracy.

16. Our approach
• label propagation using amendable clamping (LPAC).
• Objective: decreasing the impact of missing labels on accuracy.
• Our approach is 45% higher than comparative
approach.
• document : 70%
• label : 50%
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17. 17
Labeled data
Unlabeled data
Proposed algorithm: LPAC

18. Proposed algorithm: LPAC
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top-k of
Labeled data
Unlabeled data

19. Proposed algorithm: LPAC
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top-k of
Labeled data at nth iteration Instead of clamping
we set average valuesUnlabeled data at nth iteration

20. Proposed algorithm: LPAC
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top-k of
Labeled data at nth iteration
Unlabeled data at nth iteration
Instead of clamping
we set average values

21. Experimental setting
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Dataset SIAM 2007 Text Mining Competition dataset
Labeled data 4819
Unlabeled data 4819
Classes 22
Average number of
label
3.41
• We apply latent dirichlet allocation (LDA) to our data.

22. Experimental setting
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• The ratio of documents that has missing labels.
Label A
Label B
Label A
Label C
Ex.) Extraction ratio is 40%. (2 documents / 5 documents)

23. Experimental setting
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• The ratio of documents that has missing labels.
Label A
Label B
Label A
Label C
Ex.) Extraction ratio is 40%. (2 documents / 5 documents)

24. Experimental setting
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• The ratio of missing labels.
Ex.) Removal ratio is 50%.
1. Label A
2. Label B
3. Label C
4. Label D
(2 labels / 4 labels)

25. Experimental setting
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• The ratio of missing labels.
1. Label A
2. Label B
3. Label C
4. Label D
(2 labels / 4 labels)Ex.) Removal ratio is 50%.

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Experimental setting
Comparative algorithm
LP (traditional)
DLP (Dynamic Label Propagation, state-of-
the-art)
LNP (Linear neighborhoods propagation)
Random Forest
SVM

27. Micro-averaged F-scores for six classifiers
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The x axis represents the ratio of documents that has missing labels.
The y axis represents the ratio of missing labels.

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The x axis represents the ratio of documents that has missing labels.
The y axis represents the ratio of missing labels.
Micro-averaged F-scores for six classifiers

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The x axis represents the ratio of documents that has missing labels.
The y axis represents the ratio of missing labels.
Micro-averaged F-scores for six classifiers

30. Micro-averaged F-scores for six classifiers
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The x axis represents the ratio of documents that has missing labels.
The y axis represents the ratio of missing labels.

31. Micro-averaged F-scores for six classifiers
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The x axis represents the ratio of documents that has missing labels.
The y axis represents the ratio of missing labels.

32. Micro-averaged F-scores for six classifiers
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The x axis represents the ratio of documents that has missing labels.
The y axis represents the ratio of missing labels.

33. Conclusion
• We propose a multi-label classification (LPAC) for a
moderately challenging multi-labeling task.
1. Propagating labels according to top-k similar data.
2. Updating labeled data by taking cluster assumption.
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34. Conclusion
• We propose a multi-label classification (LPAC) for a
moderately challenging multi-labeling task.
1. Propagating labels according to top-k similar data.
2. Updating labeled data by taking cluster assumption.
• Future work
1. The effective utilization of label correlation.
2. How effectively our algorithm works on a real dataset.
3. Establishing algorithm that can be trained on dataset including
both of wrong and missing.
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