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Using a similarity measure for credible classification

机译：使用相似性度量进行可靠分类

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摘要

This paper concerns classification by Boolean functions. We investigate the classification accuracy obtained by standard classification techniques on unseen points (elements of the domain, f0; 1gn, for some n) that are similar, in particular senses, to the points that have been observed as training observations. Explicitly, we use a new measure of how similar a point x 2 f0; 1gn is to a set of such points to restrict the domain of points on which we offer a classification. For points sufficiently dissimilar, no classification is given. We report on experimental results which indicate that the classification accuracies obtained on the resulting restricted domains are better than those obtained without restriction. These experiments involve a number of standard data-sets and classification techniques. We also compare the classification accuracies with those obtained by restricting the domain on which classification is given by using the Hamming distance.
机译：本文涉及通过布尔函数进行分类。我们研究了通过标准分类技术获得的未分类点（域元素f0;对于某些n为1gn）的分类准确性，这些点在特别意义上与作为训练观测值观察到的点相似。明确地，我们使用一个新的度量来衡量点x 2 f0的相似度; 1gn是一组这样的点，用于限制我们提供分类的点的范围。对于足够不同的点，则不进行分类。我们报告了实验结果，这些结果表明，在得到的受限域上获得的分类准确度要好于在没有限制的情况下获得的分类准确度。这些实验涉及许多标准数据集和分类技术。我们还将分类准确度与通过限制使用汉明距离进行分类的域所获得的准确度进行比较。

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