1

DiVo: A Novel Distance based Voting Methodfor One Class Classification

Merter Sualp and Tolga Can

IEEE Transactions on Knowledge and Data Engineering

Paper study- 2012/12/22

OUTLINEIntroductionMethod of DiVoResultsDiscussion

IntroductionWhen 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.

Method of DiVo

Method of DiVo - trainingBoundary Rule:

The distance from a class member q to a training sample t, is less than or equal to the farthest distance from t to any of the other training samples.

distance metric : Euclidean / MahalanobisEuclidean distance :

Method of DiVo - trainingA set T of k positive samples

A set B of k boundary distances

Method of DiVo - testing

threshold “ratio” : 重疊、密集程度ratio

The label y of sample x0:negative , 1:positive

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.

Resultspreprocessing

normalize all attribute values between 0 and 1.3-fold cross-validation

1 for training , 2 for testing

f-measure

f-measure

Results

DiscussionDiVo-M

DiscussionDiVo-E

DiscussionBiomed Data (藍 )

DiscussionDermatology Data (黃 )