论文标题
管理数据分类和解决数据不平衡问题的方法的调查
A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem
论文作者
论文摘要
阶级不平衡的问题是关注现实世界中众多应用程序的广泛问题。在这种情况下，几乎所有的例子都被标记为一个称为多数类的类，而示例较少的示例通常被标记为另一个班级，而更重要的班级称为少数群体。 Over the last few years, several types of research have been carried out on the issue of class imbalance, including data sampling, cost-sensitive analysis, Genetic Programming based models, bagging, boosting, etc. Nevertheless, in this survey paper, we enlisted the 24 related studies in the years 2003, 2008, 2010, 2012 and 2014 to 2019, focusing on the architecture of single, hybrid, and ensemble method design to understand the current status of improving classification output in machine学习解决班级失衡问题的技术。本调查论文还包括对各种方法和其他几种实验条件下的分类算法以及不同研究论文中使用的数据集的统计分析。
The problem of class imbalance is extensive for focusing on numerous applications in the real world. In such a situation, nearly all of the examples are labeled as one class called majority class, while far fewer examples are labeled as the other class usually, the more important class is called minority. Over the last few years, several types of research have been carried out on the issue of class imbalance, including data sampling, cost-sensitive analysis, Genetic Programming based models, bagging, boosting, etc. Nevertheless, in this survey paper, we enlisted the 24 related studies in the years 2003, 2008, 2010, 2012 and 2014 to 2019, focusing on the architecture of single, hybrid, and ensemble method design to understand the current status of improving classification output in machine learning techniques to fix problems with class imbalances. This survey paper also includes a statistical analysis of the classification algorithms under various methods and several other experimental conditions, as well as datasets used in different research papers.
