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PRL
2011

A Bayes-true data generator for evaluation of supervised and unsupervised learning methods

7 years 9 months ago
A Bayes-true data generator for evaluation of supervised and unsupervised learning methods
Benchmarking pattern recognition, machine learning and data mining methods commonly relies on real-world data sets. However, there are some disadvantages in using real-world data. On one hand collecting real-world data can become difficult or impossible for various reasons, on the other hand real-world variables are hard to control, even in the problem domain; in the feature domain, where most statistical learning methods operate, exercising control is even more difficult and hence rarely attempted. This is at odds with the scientific experimentation guidelines mandating the use of as directly controllable and as directly observable variables as possible. Because of this, synthetic data possesses certain advantages over real-world data sets. In this paper we propose a method that produces synthetic data with guaranteed global and class-specific statistical properties. This method is based on overlapping class densities placed on the corners of a regular k-simplex. This generator can...
Janick V. Frasch, Aleksander Lodwich, Faisal Shafa
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Where PRL
Authors Janick V. Frasch, Aleksander Lodwich, Faisal Shafait, Thomas M. Breuel
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