1. Xiao-Yu Zhang, Shupeng Wang, Xiaochun Yun
Bidirectional Active Learning: A Two-Way Exploration Into Unlabeled and Labeled Data Set By
Xiao-Yu Zhang, Shupeng Wang, Xiaochun Yun
Presented By
Ruhani Faiheem Rahman

2. But what about mislabeled data?
Abstract
Labelling the data is one of the major problem in Machine Learning
We have huge amount of unlabeled data and few has the label
Active Learning helps in that case.
But what about mislabeled data?
This noise will propagate to the model
This paper explores both labeled and unlabeled data sets simultaneously

3. Introduction Classic Machine Learning
Supervised Learning
Unsupervised Learning
If unlabeled data explored along with the labeled data, considerable amount of improvement possible
Actively select the most informative instances to improve the model

4. Unidirectional Active Learning
Traditional active learning
Chose instance from the sample, to learn the model effectively
Uncertainty sampling - choose the least certain instance
Query by Committee - a voting is done among the classes. Most disagreed samples are selected
Decision Theoretic Approach - instance which reduce the model’s generalization error if its label was known
Noise in the labeled data can jeopardize the learning performance

5. Unidirectional Active Learning

6. Bidirectional Active Learning
Forward Learning
Backward Learning
Backward Instance Detecting
Instance-level detecting
Label-level detecting
Backward Instance Processing
Undo
Redo
Relabel
Reselect
Backward Learning Algorithm

7. Forward Learning Similar to Unidirectional Active Learning (UDAL)
Selects a forward instance from Unlabeled data set based on the selection mechanism described before
Add the instance to label data set and removes it from unlabeled data set
Train a new model

8. Backward Learning Detect a Backward Instance
Explores the labeled data set instead of Unlabeled data set
Detect an instance from the labeled data set based on
Instance level detecting
FInd the instance without which the entropy over unlabeled data set would be minimized
Label level detecting
Find the most suspiciously mislabeled instance
If the label is changed and the entire error is minimized

9. Backward Learning Process the backward instance Undo
Eliminate the negative influence by removing it from the training set
Suitable for instance level method
Redo
Relabel
Backward instance is returned to be labeled for the second time
If new label is the same as previous then it will be copied twice
Otherwise replace it with the new label
Reselect
Find the nearest neighbour of the backward instance in the unlabeled data set
Probability of the neighbour’s label and the backward instance is higher
Add this instance to the train model

11. BDAL Algorithm

12. Experiments Synthesis Data Classification
Handwritten Digit Classification
Image Classification
Patent Document Classification

13. Synthesis Data Classification
Two class synthesis data
410 instances
205 instances for each class
5 instances from each class are selected randomly for initial training

14. Synthesis Data Classification

15. B. Handwritten Digit Classification
MNIST data set
10,000 instances of test data
Each image has 28 * 28 pixels
100 images randomly picked for initial training
For model update, 100 images are labeled with 5% noise
Result is averaged over 20 runs

16. B. Handwritten Digit Classification

17. C. Image Classification
50 categories of images. Like car, ship, human etc
Each category contains 100 images
10 images from each category used for initial training the model
So, total 500 images as initial training data
For each model update 500 images with 5% noise.
Result is averaged over 50 runs.

18. C. Image Classification

19. D. Patent Document Classification
5000 patents data from Innography database.
All those data are manually classified by domain experts into 5 classes.
5484 terms are extracted as raw text file, then use PCA for dimention reduction
150-D feature vector
50 instances are picked randomly for initial training data
For each model update, 100 instances are labeled with 5% noise
Result is averaged over 20 runs

20. D. Patent Document Classification

21. Conclusion BDAL performs better in all the experiments
BDAL > UDAL > NonAL
Redo Strategy receives slightly improved performance than UDAL
Undo Strategy outperforms most of the experiments

22. Future Plan
Different strategies can be adopted during the backward learning process based on the noise on the data
Fast approximation algorithm will be studied for computational efficiency

23. Thank You

24. Any Question?