Using decision boundary to analyze classifiers
Zhiyong Yan, Congfu Xu · 2008
In this paper we propose to use decision boundary to analyze classifiers. Two algorithms called decision boundary point set (DBPS) and decision boundary neuron set (DBNS) are proposed to obtain the data on the decision boundary. Based on DBNS, a visualization algorithm called SOM based decision boundary visualization (SOMDBV) is proposed to visualize the high-dimensional classifiers. The decision boundary can give an insight into classifiers, which cannot be supplied by accuracy. And it can be applied to select proper classifier, to analyze the tradeoff between accuracy and comprehensibility, to discovery the chance of over-fitting, to calculate the similarity of models generated by different classifiers. Experimental results demonstrate the usefulness of the method.