A Classifier Capable of Handling New Attributes

During knowledge acquisition, a new attribute can be added at any time. In such a case, rule generated by the training data with the former attribute set can not be used. Moreover, the rule can not be combined with the new data set with the newly added attribute(s) using the existing algorithms. In this paper, we propose further development of the new inference engine, UChoo, that can handle the above case naturally. Rule generated from the former data set can be combined with the new data set to form the refined rule. This paper shows how this can be done consistently by the extended data expression, and also shows the experimental result to claim the effectiveness of the algorithm