Inconsistent Outliers
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Transcript of 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