Robustness Measure of Decision Rules

Rough set approaches provide useful tools to find minimal decision rules. The obtained minimal decision rules are used to classify unseen objects. On the other hand, the condition parts of the minimal decision rules are sometimes used to design new objects which will be classified into the target decision class. While we are interested in the goodness of the set of obtained minimal decision rules in the former case, we are interested in the goodness of an individual minimal decision rule in the latter case. In this paper, we propose robustness measure as a new type of evaluation index for decision rules. The measure evaluates to what extent the decision rule maintains the goodness of classification against the partially-matched data. Numerical experiments are conducted to examine the effectiveness of robustness measure.