Potential improvement of classifier accuracy by using fuzzy measures

Typical digit recognizers classify an unknown digit pattern by computing its distance from the cluster centers in a feature space. In this paper, we propose a methodology that has many salient aspects. First, the classification rule is dependent on the "difficulty" of the unknown sample. Samples "far" from the center, which tend to fall on the boundaries of classes are error prone and, hence, "difficult". An "overlapping zone" is defined in the feature space to identify such difficult samples. A table is precomputed to facilitate an efficient lookup of the class corresponding to all the points in the overlapping zone. The lookup function itself is defined by a modification of the KNN rule. A characteristic function defining the new boundaries is computed using the topology of the set of samples in the overlapping zones. Our two-pronged approach uses different classification schemes with the "difficult" and "easy" samples. The method described has improved the performance of the gradient structural concavity digit recognizer described by Favata et al. (1996).