ABSTRACT

A nonparametric approach is proposed to combine several individual classifiers in order to construct an asymptotically more accurate classification rule in the sense that its misclassification error rate is, asymptotically, at least as low as that of the best individual classifier. The proposed method uses a nearest neighbour type approach to estimate the conditional expectation of the class associated with a new observation (conditional on the vector of individual predictions). Both mechanics and the theoretical validity of the proposed approach are discussed. As an interesting byproduct of our results, it is shown that the proposed method can also be applied to any single classifier in which case the resulting new classifier will be at least as good as the original one. Several numerical examples, involving both real and simulated data, are also given. These numerical studies further confirm the superiority of the proposed classifier.

摘要

提出了一种非参数方法来组合多个单独的分类器，以构建渐近更准确的分类规则，因为它的误分类错误率渐近地至少与最佳单独分类器的误分类错误率一样低。所提出的方法使用最近邻类型方法来估计与新观察相关联的类的条件期望（以单个预测的向量为条件）。讨论了所提出方法的力学和理论有效性。作为我们结果的一个有趣的副产品，它表明所提出的方法也可以应用于任何单个分类器，在这种情况下，产生的新分类器至少与原始分类器一样好。几个数值例子，涉及真实和模拟数据，也给了。这些数值研究进一步证实了所提出的分类器的优越性。