Inconsistent Outliers

Inconsistency and OutliersActive Learning by Outlier Detection

Inconsistency Robustness Symposium 2011

Neil RubensAssistant Professor

University of Electro-CommunicationsTokyo, Japan

Outline

Inconsistency Robustness is a multi-disciplinary issue. We discuss some of the aspect of Inconsistency Robustness from the perspective of Machine Learning:

• What is Inconsistency• Can Inconsistency be Useful• Measuring Inconsistency

Inconsistency-Outlier

Outlier Types

• Spatial Outlier– unlabeled data

• Model Outlier– labeled data

Our Focus

Causes of Outliers

• Faulty data– Entry error, malfunction, etc.

• Incorrect Model

http://www.dkimages.com/discover/previews/852/20223083.JPG

• Chance/Deviation

Our Focus

Typical Treatment of Outliers

• Assume that the learned model is correct and discard points that don’t agree with the model

Atypical Treatment of Outliers

• Assume that data is right, and that the model is wrong

Our Focus

Rubens et al, AJS 2011

If there is no inconsistency between the training and testing data then the most complex model would tend be selected.

Change Detection / Model Correction

Is inconsistency caused by noise (or minor factors) or by changes in the underlying model

http://www.satimagingcorp.com/galleryimages/high-resolution-landsat-satellite-imagery-oman.jpg

– Applications: medical diagnostics, intrusion detection, network analysis, finance

Conclusion

• Inconsistency could be useful for:– Hypothesis Learning– Model Selection– Model Correction

Neil RubensAssistant ProfessorActive Intelligence GroupLaboratory for Knowledge ComputingUniversity of Electro-CommunicationsTokyo, Japan

http://ActiveIntelligence.org