进化多任务 AUC 优化
Evolutionary Multitasking AUC Optimization
AI总结：
针对大规模 AUC 优化代价高且非凸的问题，提出进化多任务框架 EMTAUC，通过廉价采样任务辅助原始昂贵任务，并动态调整其数据结构以增强知识迁移。
摘要：
学习优化受试者工作特征曲线下面积（AUC）在不平衡数据上的性能，近年来受到广泛关注。尽管已有若干 AUC 优化方法，但由于其成对学习的特性，扩展 AUC 优化的规模仍是一个开放性问题。在大规模数据集中最大化 AUC 可被视为一个非凸且代价高昂的问题。受成对学习特性的启发，本文构建了一个廉价的 AUC 优化任务，该任务使用从大规模数据集中采样得到的小规模数据集，以促进原始、大规模且昂贵的 AUC 优化任务的 AUC 精度。本文提出了一个进化多任务框架（称为 EMTAUC），以充分利用所构建的廉价任务与昂贵任务之间的信息，从而获得更高性能。在 EMTAUC 中，一个任务是从采样数据集中优化 AUC，另一个任务是在原始数据集上最大化 AUC。此外，由于廉价任务包含的知识有限，本文提出了一种动态调整廉价任务数据结构的策略，以向多任务 AUC 优化环境引入更多知识。所提方法的性能在一系列二分类数据集上进行了评估。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在 https://github.com/xiaofangxd/EMTAUC 获取。
英文摘要：
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.
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。
