The pure accuracy measure is used to eliminate random consistency from the accuracy measure. Biases to both majority and minority classes in the pure accuracy are lower than that in the accuracy measure. In this paper, we demonstrate that compared with the accuracy measure and F-measure, the pure accuracy measure is class distribution insensitive and discriminative for good classifiers. The advantages make the pure accuracy measure suitable for traditional classification. Further, we mainly focus on two points: exploring a tighter generalization bound on pure accuracy based learning paradigm and designing a learning algorithm based on the pure accuracy measure. Particularly, with the self-bounding property, we build an algorithm-independent generalization bound on the pure accuracy measure, which is tighter than the existing bound of an order O(1/N−−√)O(1/\sqrt{N}) (N is the number of instances). The proposed bound is free from making a smoothness or convex assumption on the hypothesis functions. In addition, we design a learning algorithm optimizing the pure accuracy measure and use it in the selective ensemble learning setting. The experiments on sixteen benchmark data sets and four image data sets demonstrate that the proposed method statistically performs better than the other eight representative benchmark algorithms.

中文翻译：

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纯准确率的泛化性能及其在选择性集成学习中的应用


纯准确度测量用于消除准确度测量中的随机一致性。纯准确度中对多数类和少数类的偏差均低于准确度测量中的偏差。在本文中，我们证明，与准确度测量和 F 测量相比，纯粹的准确度测量对类分布不敏感，并且对于好的分类器具有区分性。这些优点使得纯准确度测量适合传统分类。此外，我们主要关注两点：探索基于纯准确性的学习范式的更严格的泛化界限，并设计基于纯准确性度量的学习算法。特别是，利用自边界属性，我们在纯精度度量上构建了一个与算法无关的泛化边界，该边界比 O(1/N−−√)O(1/\sqrt{N })（N 是实例数）。所提出的界限无需对假设函数进行平滑或凸假设。此外，我们设计了一种优化纯准确度测量的学习算法，并将其用于选择性集成学习设置。在十六个基准数据集和四个图像数据集上的实验表明，该方法在统计上比其他八个代表性基准算法表现更好。