DiVo : A Novel Distance based Voting Method for One Class Classification

1. Paper study- 2012/12/22 DiVo: A Novel Distance based Voting Methodfor One Class Classification MerterSualp and Tolga Can IEEE Transactions on Knowledge and Data Engineering

2. OUTLINE Introduction Method of DiVo Results Discussion

3. Introduction • When there exist sufficiently many training examples, the estimation error of the model tends to decrease. • Although, it may not be possible or feasible to collect sufficient training data, especially in application domains. • Negative training data is artificially generated. • fidelity • Methods which are specifically developed to work with one class training datasets bypass the artificial data generation stage.

4. Method of DiVo

5. Method of DiVo - training • Boundary Rule: • The distance from a class member q to a training sample t, is less than or equal to the farthest distance from tto any of the other training samples. • distance metric : Euclidean / Mahalanobis • Euclidean distance :

6. Method of DiVo - training • A set T of k positive samples • Aset B of k boundary distances

7. Method of DiVo - testing • threshold “ratio” : 重疊、密集程度 • ratio • The label y of sample x • 0:negative , 1:positive

8. Results We simulate the one class classification problem by selecting each class as the target class and the rest of them as the non-targets and using a subset of the target class samples during the training phase.

9. Results • preprocessing • normalize all attribute values between 0 and 1. • 3-fold cross-validation • 1 for training , 2 for testing • f-measure • f-measure

10. Results

11. Discussion DiVo-M

12. Discussion DiVo-E

13. Discussion Biomed Data(藍)

14. Discussion Dermatology Data (黃)