Generalized Statistical Methods for Unsupervised Minority Class Detection in Mixed Data Sets

Proceedings of the 2008 IAPR Workshop on Cognitive Information Processing. pp. 126-131. Santorini, Greece. 2008
Generalized Statistical Methods for Unsupervised Minority Class Detection in Mixed Data Sets
Cecile Levasseur, Uwe Mayer, Brandon Burdge, Ken Kreutz-Delgado

Minority class detection is the problem of detecting the occurrence of rare key events differing from the majority of a data set. This paper considers the problem of unsupervised minority class detection for multidimensional data that are highly nongaussian, mixed (continuous and/or discrete), noisy, and nonlinearly related, such as occurs, for example, in fraud detection in typical financial data.

A statistical modeling approach is proposed which is a subclass of graphical model techniques. It exploits the properties of exponential family distributions and generalizes techniques from classical linear statistics into a framework referred to as Generalized Linear Statistics (GLS). The methodology exploits the split between the data space and the parameter space for exponential family distributions and solves a nonlinear problem by using classical linear statistical tools applied to data that has been mapped into the parameter space.

A fraud detection technique utilizing low-dimensional information learned by using an Iteratively Reweighted Least Squares (IRLS) based approach to GLS is proposed in the parameter space for data of mixed type. ROC curves for an initial simulation on synthetic data are presented, which gives predictions for results on actual financial data sets.

Another publication from the same author: Uwe Mayer

Proceedings of the Sixteenth ACM Conference on Economics and Computation (EC '15). ACM, New York, NY, USA (2015)

Canary in the e-Commerce Coal Mine: Detecting and Predicting Poor Experiences Using Buyer-to-Seller Messages

Dimitriy Masterov, Uwe Mayer, Steve Tadelis

Reputation and feedback systems in online marketplaces are often biased, making it difficult to ascertain the quality of sellers. We use post-transaction, buyer-to-seller message traffic to detect signals of unsatisfactory transactions on eBay. We posit that a message sent after the item was paid for serves as a reliable indicator that the buyer may be unhappy with that purchase, particularly when the message included words associated with a negative experience. The fraction of a seller's message traffic that was negative predicts whether a buyer who transacts with this seller will stop purchasing on eBay, implying that platforms can use these messages as an additional signal of seller quality.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.