Improving identification of difficult small classes by balancing class distribution
Improving identification of difficult small classes by balancing class distribution
复制标题
DOI：
10.1007/3-540-48229-6_9
复制
发表时间：
2001-01-01
期刊：
影响因子：
--
通讯作者：
Laurikkala, J
中科院分区：
文献类型：
--
作者：
Laurikkala, J
我们研究了三种方法，通过平衡不平衡的类分布和数据约简来提高对困难小类的识别。在10个数据集的实验中，新的邻域清洗规则(NCL)方法的性能优于简单的随机和片面选择方法。所有的缩减方法都提高了对小类的识别率(20%-30%)，但差异不显著。然而，在准确性、真阳性率和真阴性率方面，用3最近邻法和C4.5方法获得的准确性和真阴性率的显著差异有利于NCL。结果表明，NCL是一种有用的方法，可以改进困难小类的建模，并构建分类器来从真实世界的数据中识别这些类。
We studied three methods to improve identification of difficult small classes by balancing imbalanced class distribution with data reduction. The new method, neighborhood cleaning rule (NCL), outperformed simple random and one-sided selection methods in experiments with ten data sets. All reduction methods improved identification of small classes (20-30%), but the differences were insignificant. However, significant differences in accuracies, true-positive rates and true-negative rates obtained with the 3-nearest neighbor method and C4.5 from the reduced data favored NCL. The results suggest that NCL is a useful method for improving the modeling of difficult small classes, and for building classifiers to identify these classes from the real-world data.