The imbalanced issue among data is common in many machine-learning applications, where samples from one or more classes are rare. To address this issue, many imbalanced machine-learning methods have been proposed. Most of these methods rely on cost-sensitive learning. However, we note that it is infeasible to determine the precise cost values even with great domain knowledge for those cost-sensitive machine-learning methods. So in this method, due to the superiority of F-measure on evaluating the performance of imbalanced data classification, we employ F-measure to calculate the cost information and propose a cost-sensitive hypergraph learning method with F-measure optimization to solve the imbalanced issue. In this method, we employ the hypergraph structure to explore the high-order relationships among the imbalanced data. Based on the constructed hypergraph structure, we optimize the cost value with F-measure and further conduct cost-sensitive hypergraph learning with the optimized cost information. The comprehensive experiments validate the effectiveness of the proposed method.

中文翻译：

---

使用 F-Measure 优化的成本敏感超图学习

数据之间的不平衡问题在许多机器学习应用程序中很常见，其中来自一个或多个类别的样本很少见。为了解决这个问题，已经提出了许多不平衡机器学习方法。这些方法中的大多数都依赖于对成本敏感的学习。然而，我们注意到，对于那些对成本敏感的机器学习方法，即使拥有丰富的领域知识，也无法确定精确的成本值。因此在该方法中，由于F-measure在评估不平衡数据分类性能方面的优越性，我们采用F-measure来计算成本信息，并提出了一种具有F-measure优化的成本敏感超图学习方法来解决不平衡问题问题。在这种方法中，我们采用超图结构来探索不平衡数据之间的高阶关系。基于构建的超图结构，我们使用 F-measure 优化成本值，并进一步使用优化的成本信息进行成本敏感的超图学习。综合实验验证了所提方法的有效性。