In our previous work, we developed a fuzzy classifier with ellipsoidal regions that has a training capability. In this paper, we extend the fuzzy classifier to diagnosis problems, in which the training data belonging to abnormal classes are difficult to obtain while the training data belonging to normal classes are easily obtained. Assuming that there are no data belonging to abnormal classes, we first train the fuzzy classifier with only the data belonging to normal classes. We then introduce the threshold of the minimum-weighted distance from the centers of the clusters for the data belonging to normal classes. If the unknown data is within the threshold, we classify the data into normal classes and, if not, abnormal classes. The operator checks whether the diagnosis is correct. If the incoming data is classified into the same normal class both by the classifier and the operator, nothing is done. But if the input data is classified into the different normal classes by the classifier and the operator, or if the incoming data is classified into an abnormal class, but the operator classified it into a normal class, the slopes of the membership functions of the fuzzy rules are tuned. If the operator classifies the data into an abnormal class, the classifier is retrained adding the newly obtained data irrespective of the classifier's classification result. The online training is continued until a sufficient number of the data belonging to abnormal classes are obtained. Then the threshold is optimized using the data belonging to both normal and abnormal classes. We evaluate our method using the Fisher iris data, blood cell data, and thyroid data, assuming some of the classes are abnormal.
['Shigeo Abe', 'Ruck Thawonmas', 'Masahiro Kayama']
A fuzzy classifier with ellipsoidal regions for diagnosis problems