Analysis of classification margin for classification accuracy with applications ?
Classification margin is commonly used for describing the classification capability of a committee of classifiers. In this paper, we study the relation between classification margin and misclassification error, focusing on exploring useful information about misclassification error from the known classification margin. We propose a maxmin type bound concerning the minimal misclassification rate, and present some useful properties. Finally, we seek the way to improve classification performance by incorporating the classification margins, and devise an algorithm for improving average classification accuracy based on the proposed bound. Experimental results show the effectiveness of the proposed algorithm and also validate our analytic results.
classification margin classifier ensemble bagging optimal bound