基于不确定性最小化的特征和原型剪枝方法
Combining Feature and Prototype Pruning by Uncertainty Minimization
摘要：
我们专注于在本文中使用的数据集减少技术在k-最近邻分类。在这样的背景下，特征和原型选择总是由标准的存储缩减算法独立处理。虽然这种证明理论上是合理的事实，每个子问题是NP-难的，我们假设在本文中，联合存储减少实际上是更直观的，可以在实践中提供更好的结果比两个独立的过程。此外，它避免了大量的距离计算，逐步删除无用的实例在特征修剪。虽然标准的选择算法往往优化的准确性，以区别的解决方案，我们在本文中使用的标准的基础上的不确定性措施内的最近邻图。这一选择来自最近的结果，这些结果已经证明，准确性并不总是优化的合适标准。在我们的方法中，一个功能或实例被删除，如果它的删除改善了图的信息。本文提出了大量的实验和统计分析表明，我们的方法的相关性，其容忍度在噪声的存在下。
英文摘要：
We focus in this paper on dataset reduction techniques for use in k-nearest neighbor classification. In such a context, feature and prototype selections have always been independently treated by the standard storage reduction algorithms. While this certifying is theoretically justified by the fact that each subproblem is NP-hard, we assume in this paper that a joint storage reduction is in fact more intuitive and can in practice provide better results than two independent processes. Moreover, it avoids a lot of distance calculations by progressively removing useless instances during the feature pruning. While standard selection algorithms often optimize the accuracy to discriminate the set of solutions, we use in this paper a criterion based on an uncertainty measure within a nearest-neighbor graph. This choice comes from recent results that have proven that accuracy is not always the suitable criterion to optimize. In our approach, a feature or an instance is removed if its deletion improves information of the graph. Numerous experiments are presented in this paper and a statistical analysis shows the relevance of our approach, and its tolerance in the presence of noise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
