Improving classifier performance through repeated sampling

摘要

In traditional pattern recognition, the classification decision is based on a single observation of the input. In this paper, we show that by relaxing this assumption, the performance of the classifier can be improved substantially. We present a detailed analysis of one particular method for achieving this: taking a consensus vote on the classifier's output for repeated samples of the input. We prove that this approach always yields a net improvement in recognition accuracy for common distributions of interest. Upper and lower bounds on the improvement are also discussed. Under certain conditions, it is even possible to “beat” the Bayes error bound associated with the classifier. We conclude by presenting results from three sets of experiments examining the effectiveness of the idea.