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Using decision boundary to analyze classifiers | IEEE Conference Publication | IEEE Xplore

Using decision boundary to analyze classifiers


Abstract:

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)...Show More

Abstract:

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.
Date of Conference: 17-19 November 2008
Date Added to IEEE Xplore: 30 December 2008
ISBN Information:
Conference Location: Xiamen

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