Learning to optimize the area under the receiver operating characteristics curve (AUC) performance for imbalanced data has attracted much attention in recent years. Although there have been several methods of AUC optimization, scaling up AUC optimization is still an open issue due to its pairwise learning style. Maximizing AUC in the large-scale dataset can be considered as a non-convex and expensive problem. Inspired by the characteristic of pairwise learning, the cheap AUC optimization task with a small-scale dataset sampled from the large-scale dataset is constructed to promote the AUC accuracy of the original, large-scale, and expensive AUC optimization task. This paper develops an evolutionary multitasking framework (termed EMTAUC) to make full use of information among the constructed cheap and expensive tasks to obtain higher performance. In EMTAUC, one mission is to optimize AUC from the sampled dataset, and the other is to maximize AUC from the original dataset. Moreover, due to the cheap task containing limited knowledge, a strategy for dynamically adjusting the data structure of inexpensive tasks is proposed to introduce more knowledge into the multitasking AUC optimization environment. The performance of the proposed method is evaluated on a series of binary classification datasets. The experimental results demonstrate that EMTAUC is highly competitive to single task methods and online methods. Supplementary materials and source code implementation of EMTAUC can be accessed at https://github.com/xiaofangxd/EMTAUC .

中文翻译：

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进化多任务AUC优化【研究前沿】


近年来，学习优化不平衡数据的受试者工作特征曲线（AUC）性能下的面积引起了广泛关注。尽管已经有多种 AUC 优化方法，但由于其成对学习风格，扩大 AUC 优化仍然是一个悬而未决的问题。在大规模数据集中最大化 AUC 可以被视为一个非凸且昂贵的问题。受成对学习特性的启发，构建了从大规模数据集采样的小规模数据集的廉价 AUC 优化任务，以提升原始大规模且昂贵的 AUC 优化任务的 AUC 精度。本文开发了一种进化多任务框架（称为 EMTAUC），以充分利用构建的廉价和昂贵任务之间的信息来获得更高的性能。在 EMTAUC 中，一个任务是从采样数据集中优化 AUC，另一个任务是从原始数据集中最大化 AUC。此外，由于廉价任务包含的知识有限，因此提出了一种动态调整廉价任务数据结构的策略，以将更多知识引入多任务AUC优化环境。该方法的性能在一系列二元分类数据集上进行评估。实验结果表明，EMTAUC 相对于单任务方法和在线方法具有很强的竞争力。 EMTAUC 的补充材料和源代码实现可访问： https://github.com/xiaofangxd/EMTAUC 。