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====== Extreme Value Correction: A Method for Correcting Optimistic Estimations in Rule Learning ======

Machine learning algorithms rely on their ability to evaluate the
constructed hypotheses for choosing the optimal hypothesis during
learning and assessing the quality of the model afterwards. Since these
estimates, in particular the former ones, are based on the training
data from which the hypotheses themselves were constructed, they are
inevitably optimistic. The paper shows three different solutions; two for
the artificial boundary cases with the smallest and the largest optimism
and a general correction procedure called extreme value correction (EVC) based on extreme value
distribution. We demonstrate the application of the technique to rule
learning, specifically on estimating classification accuracy of rules, and evaluate it on
an artificial data set and on a number of UCI data sets. We observed that the
correction successfully improves the accuracy estimates. In the last part of 
evaluation, we describe an approach for combining rules into a linear 
global classifier and show that using EVC estimates leads to more 
accurate classifiers.

Paper submitted to journal.

  * [[https://github.com/martinmozina/orange3-evcrules |Source code of Orange 3 add-on]]

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