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 $O(1/\sqrt{N})$ (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.

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纯准确度的泛化性能及其在选择性集成学习中的应用

纯精度度量用于从精度度量中消除随机一致性。纯准确性中对多数类和少数类的偏差均低于准确性测量中的偏差。在本文中，我们证明与精度度量和 F 度量相比，纯精度度量对类别分布不敏感且对良好的分类器具有判别力。这些优点使得纯准确度度量适用于传统分类。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，以及设计基于纯准确性度量的学习算法。特别是，利用自界属性，我们在纯精度度量上构建了一个算法无关的泛化界，它比现有的阶数界更严格$O(1/\sqrt{N})$（N 是实例数）。所提出的边界没有对假设函数做出平滑或凸假设。此外，我们设计了一种优化纯准确度度量的学习算法，并将其用于选择性集成学习设置。在 16 个基准数据集和 4 个图像数据集上的实验表明，所提出的方法在统计上比其他 8 个具有代表性的基准算法表现更好。