In rough set approaches, decision rules are induced from a given data table showing the relation between attribute values and classes of objects. The induced decision rules are used for the classification of new objects by their attribute values. However, some of new objects do not match any decision rule conditions because the given data table does not always include all possible patterns. In those cases, no estimated classes are obtained. Classes of such new objects are estimated by using partially matched decision rules. In this paper, to raise the classification accuracy, we propose to add supplementary rules which can work well for the mismatched new objects in the class estimation. We define the supplementary rules and propose a method for inducing them. We examine the performance of the classifier with supplementary rules by comparisons with the classifier without supplementary rules.

In rough set approaches, decision rules are induced from a given data set consisting of attribute values and a decision value. Induced rules are used to classify new objects, but this classification is not perfect, perhaps because the given data set does not include all possible patterns. No induced decision rules are matched totally for objects having missing patterns, and partially matched decision rules are used to estimate their classes. The classification accuracy of such an object is usually lower than that of an object totally matching decision rules. To improve the classification accuracy, we propose adding supplementary rules to the induced rules, defining the supplementary rules to improve the classification accuracy of objects only partially matching decision rules. We propose an algorithm for inducing supplementary rules, considering four classifiers consisting of supplementary rules together with originally induced rules.We investigate their performance. We also compare their classification accuracies to that of conventional classifier with originally induced rules.

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