Evaluation of Allocation Rules Under Some Cost Constraints

Abstract

Allocation of individuals or objects to labels or classes is a central problem in statistics, particularly in supervised classification methods such as Linear and Quadratic Discriminant analysis, Logistic Discrimination, Neural Networks, Support Vector Machines, and so on. Misallocations occur when allocation class and origin class differ. These errors could result from different situations such as quality of data, definition of the explained categorical variable or choice of the learning sample. Generally, the cost is not uniform depending on the type of error and consequently the use only of the percentage of correctly classified objects is not enough informative.

In this paper we deal with the evaluation of allocation rules taking into account the error cost. We use a statistical index which generalizes the percentage of correctly classified objects.