论文标题
一致的多类算法，用于复杂指标和约束
Consistent Multiclass Algorithms for Complex Metrics and Constraints
论文作者
论文摘要
我们提供了具有复杂性能指标和约束的多类学习算法，其中目的和约束是由混淆矩阵的任意函数定义的。该设置包括许多常见的性能指标，例如多类G-Mean和Micro F1量化，以及分类器的精确度和回忆和最新公平差异衡量标准。我们通过将学习问题视为一组可行的混淆矩阵的优化问题，为这类复杂的设计目标设计一致的算法提供了一个一般框架。我们在对性能指标和约束的不同假设下提供了多个框架的实例化，并且在每种情况下，都显示了融合到最佳（可行）分类器（以及因此渐近一致性）的速率。关于各种多类分类任务和公平限制的问题的实验表明，我们的算法与最先进的基线相比有利。
We present consistent algorithms for multiclass learning with complex performance metrics and constraints, where the objective and constraints are defined by arbitrary functions of the confusion matrix. This setting includes many common performance metrics such as the multiclass G-mean and micro F1-measure, and constraints such as those on the classifier's precision and recall and more recent measures of fairness discrepancy. We give a general framework for designing consistent algorithms for such complex design goals by viewing the learning problem as an optimization problem over the set of feasible confusion matrices. We provide multiple instantiations of our framework under different assumptions on the performance metrics and constraints, and in each case show rates of convergence to the optimal (feasible) classifier (and thus asymptotic consistency). Experiments on a variety of multiclass classification tasks and fairness-constrained problems show that our algorithms compare favorably to the state-of-the-art baselines.
