Classifying non-Gaussian and Mixed Data Sets in their Natural Parameter Space

Proceedings of the Nineteenth IEEE Int’l Workshop on Machine Learning for Signal Processing, Grenoble, France. September 2009
Classifying non-Gaussian and Mixed Data Sets in their Natural Parameter Space
Cécile Levasseur, Uwe Mayer, Ken Kreutz-Delgado
Abstract

We consider the problem of both supervised and unsupervised classification for multidimensional data that are nongaussian and of mixed types (continuous and/or discrete). An important subclass of graphical model techniques called Generalized Linear Statistics (GLS) is used to capture the underlying statistical structure of these complex data.

GLS exploits the properties of exponential family distributions, which are assumed to describe the data components, and constrains latent variables to a lower dimensional parameter subspace.

Based on the latent variable information, classification is performed in the natural parameter subspace with classical statistical techniques. The benefits of decision making in parameter space is illustrated with examples of categorical data text categorization and mixed-type data classification.

As a text document preprocessing tool, an extension from binary to categorical data of the conditional mutual information maximization based feature selection algorithm is presented.

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

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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

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Drawing Sound Conclusions from Noisy Judgments

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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.

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