class imbalance 类不平衡性
class imbalance learning 一种类别不均衡学习
class distribution imbalance problem 现实中样本在不同类别上的不均衡分布
In this paper,three challenges in text categorization are explored,i. e. ,class imbalance,feature selection and bottleneck of annotation.
本文挑选文本分类中的3个困难与挑战进行了研究:数据集偏斜(数据集关于类别的分布是偏斜的,即类偏斜)、特征选择、小样本问题(标注瓶颈)。
参考来源 - 文本分类技术与应用研究·2,447,543篇论文数据,部分数据来源于NoteExpress
Abstract: Recently, the problem of Class-imbalance has become a hotspot in machine learning and data mining.
摘要:最近在机器学习和数据挖掘上,非平衡类问题成为了一个研究热点。
In order to improve the class accuracy of small number of samples caused by the imbalance of sample number, weight of sample difference is introduced.
首先为克服由于样本数量不平衡性引起的小样本类别精度差的问题,引入由于样本差异的权重;
At present, researches on class imbalance problems mainly focus on two aspects: dataset processing and classification method improving.
目前国际上对类别不平衡数据的研究主要集中在两个个层面:对数据集的处理和对分类算法的改进。
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