Inconsistent Outliers

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Inconsistency and Outliers Active Learning by Outlier Detection Inconsistency Robustness Symposium 2011 Neil Rubens Assistant Professor University of Electro- Communications Tokyo, Japan

description

Outliers and Inconsistency at Inconsistency Robustness Symposium 2011 at Stanford University.

Transcript of Inconsistent Outliers

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Inconsistency and OutliersActive Learning by Outlier Detection

Inconsistency Robustness Symposium 2011

Neil RubensAssistant Professor

University of Electro-CommunicationsTokyo, Japan

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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

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Inconsistency-Outlier

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Outlier Types

• Spatial Outlier– unlabeled data

• Model Outlier– labeled data

Our Focus

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Causes of Outliers

• Faulty data– Entry error, malfunction, etc.

• Incorrect Model

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• Chance/Deviation

Our Focus

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Typical Treatment of Outliers

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

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Atypical Treatment of Outliers

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

Our Focus

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Rubens et al, AJS 2011

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

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Change Detection / Model Correction

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

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

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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