Frequency coding: an effective method for combining dichotomizers

Binary classifiers (dichotomizers) are combined for multi-class classification. Each region formed by the pairwise decision boundaries is assigned to the class with the highest frequency of training samples in that region. With more samples and classifiers, the frequencies converge to increasingly accurate non-parametric estimates of the posterior class probabilities in the vicinity of the decision boundaries. The method is applicable to non-parametric discrete or continuous class distributions dichotomized by either linear or non-linear classifiers (like support vector machines). We present a formal description of the method and place it in context with related methods. We present experimental results on machine-printed digits that demonstrate the viability of frequency coding in a classification task.