Learning Cost-Sensitive Rules for Non-forced Classification

Building accurate classifiers is very desirable for many KDD processes. Rule-based classifiers are appealing because of their simplicity and their self-explanatory nature in describing reasons for their decisions. The objective of classifiers generally has been to maximize the accuracy of predictions. When data points of different classes have different misclassification costs it becomes desirable to minimize the expected cost of the classification decisions. In this paper we present an algorithm for inducing a rule based classifier that (i) shifts the class boundaries so as to minimize the cost of misclassifications and (ii) refuses to announce a class decision for those regions of the data space that are likely to contribute significantly to the expected cost of decisions. We compare our results with other rule based classifiers such as the C4.5, CN2 and GARC for the cases of uniform and non-uniform misclassification costs of different classes.