Publications of K. Truemper

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IASI Research Report n. 480  (Previous    Next)  

Giovanni Felici, Sun F.-S., Truemper K.

A method for controlling errors in two-class classifications

ABSTRACT
Two types of errors can be associated with classifying data into two categories A and B: misclassifying an item in A as one of B and, conversely, misclassifying an item in B as one of A. Tight control over these two errors is needed in many practical situations. For example, an error of one type may have far more serious consequences than one of the other type. Most previous work on two-class classifiers does not allow for such control. In this paper, we describe a general approach that supports tight error control for two-class classification and that can utilize almost any two-class classification method as part of the decision mechanism. The main idea is to construct from the given training data a family of classifiers and then, using the training data once more, to estimate two distributions of certain vote totals. The error control is achieved via the two estimated distributions. The control is effective if the estimates of the two distributions are close to the true distributions. The approach has been tested using six well-known classification problems. In each case, the two estimated distributions were close or very close to those obtained from verification data. Accordingly, error control would be good. In addition, when the goal was minimization of total errors, then the accuracy was essentially as good as that of the best prior methods.
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