1. Active Learning for Class Imbalance Problem

2. Problem to be addressed
Motivation
class imbalance problem
referring to the situation that at least one of class having significantly less number of training examples
or examples in training data belonging to one class heavily outnumber the examples in the other class
Currently, most of the machine learning algorithms assume the training data to be balanced, support vector machine, logistic regression, naïve bayesian classifier etc,.
During the last few decades, some effective methods have been proposed to attack this problem, like up-sampling, down-sampling and asymmetric bagging, etc,.

3. Problem to be addressed
Detailed problem
Traditional machine learning algorithms are often biased toward the majority class
Since the goal of the classifiers is to reduce the training error, not taking the data distribution into consideration
Consequently, examples from the majority class are well-classified while the examples from minority class tend to be misclassified

4. Several Common Approaches
From the data perspective
Over-sampling
Under-sampling
Asymmetric Bagging
From the learning algorithm perspective
Adjusting the cost function
Tuning the related parameters

5. Background Knowledge Active Learning
Similar to semi-supervised learning method, the key idea is to use both the labeled and unlabeled data for classifier training.
Active learning is composed of four components
A small set of labeled training data, a large pool of unlabeled data, a based learning algorithm and an active learner (selection strategy)
Active learning is not a machine learning algorithm, It can be seen as a enhancing wrapper method
The difference between semi-supervised learning and active learning

6. Background Knowledge Active Learning Goals of active learning
Maximizing the learning performance while minimizing the required labeled training examples
Achieving better performance using the same amount of labeled training data
Needing less training samples to obtain the same learning performance

7. Background Knowledge

8. Background Knowledge

9. An Example SVM-based Active Learning
A small set of labeled training examples
A large pool of unlabeled data
Base learning algorithm SVM
Active Learner (selection strategy)
Instances closest to the current separating hyperplane are selected and asks for human labeling

10. Problems SVM-based Active Learning
In classical active learning methods, the most informative samples are selected from the entire unlabeled pool
In other words, each iteration of active learning involves the computation of distance of each sample to the decision boundary
For large-scale data set, it is time-consuming and computationally inefficient

11. Paper Contribution Proposed method
Instead of querying the whole unlabeled pool , a subset is first selected
Select the closed sample from using the criterion that is among the top closest instances with probability

12. Paper Contribution Proposed Method
The probability that at least one of the L instances is among the closest is
We have

13. Paper Contribution Proposed Method For example
The active learner will pick one instance, with 95% probability, that is among the top 5% closed instances to the separating hyperplane, by randomly sampling only instances regardless of the training set size

14. Experiments

15. Experiments Evaluation Metric g-means
where sensitivity and specifity are the accuracies of the positive and negative instances respectively

16. Experiments

17. Experiments

18. Experiments

19. Experiments

20. Conclusions
This paper propose a method to address the class imbalance problem using active learning technique
Experimental results show that this approach can achieve a significant decrease in the training time, while maintaining the same or even higher g-means value by using less number of training examples
Active selection of informative examples from a randomly selected subset avoid searching the whole unlabeled pool

21. Thank You

22. Q & A