An approach to evaluate the fitness of one class structure via dynamic centroids

Classification is a supervised learning approach. However, it might be beyond one’s expectation to achieve high accuracy with bad quality of training instances. On the other hand, the classes (categories) for storing the instances were usually determined by related experts in the beginning. After certain period of time, therefore, it is attractive to know whether these existing classes are still suitable for storing new instances or not. In this paper we compute the value of Class Structure Ambiguity (CSA) of one class structure via Dynamic Centroid (DC) to evaluate the ambiguities among the classes. The DC approach was to have distinct centroids for one class from individual instance’s point of view, instead of having only one centroid for that class. To verify the CSA do reveal the ambiguity of class structure, the Pearson’s correlation between the values of the CSA and that of the accuracy achieved by SVM classifier was computed according to variant class structures which were generated manually with the ambiguous degrees under control. Experimental results showed that the values of the Pearson’s correlation were almost −1 as perfect as negative correlation. That meant the CSA did reveal the ambiguous degree of one class structure.

► We propose an approach to evaluate the ambiguity of class structure objectively from a systematic point of view.
► The class structure for evaluating the ambiguity could be non-linear separeable.
► We provide a clue to reinspect the training set when the classification accuracy achieved is low.
► We give hints to reorganize the skeleton of one class structure if the ambiguity problem among classes is becoming serious.