A KNN Undersampling Approach for Data Balancing  

作　　者：Marcelo Beckmann Nelson F. F. Ebecken Beatriz S. L. Pires de Lima 

机构地区：[1]Civil Engineering Program/COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

出　　处：《Journal of Intelligent Learning Systems and Applications》2015年第4期104-116,共13页智能学习系统与应用（英文）

摘　　要：In supervised learning, the imbalanced number of instances among the classes in a dataset can make the algorithms to classify one instance from the minority class as one from the majority class. With the aim to solve this problem, the KNN algorithm provides a basis to other balancing methods. These balancing methods are revisited in this work, and a new and simple approach of KNN undersampling is proposed. The experiments demonstrated that the KNN undersampling method outperformed other sampling methods. The proposed method also outperformed the results of other studies, and indicates that the simplicity of KNN can be used as a base for efficient algorithms in machine learning and knowledge discovery.In supervised learning, the imbalanced number of instances among the classes in a dataset can make the algorithms to classify one instance from the minority class as one from the majority class. With the aim to solve this problem, the KNN algorithm provides a basis to other balancing methods. These balancing methods are revisited in this work, and a new and simple approach of KNN undersampling is proposed. The experiments demonstrated that the KNN undersampling method outperformed other sampling methods. The proposed method also outperformed the results of other studies, and indicates that the simplicity of KNN can be used as a base for efficient algorithms in machine learning and knowledge discovery.

关 键 词：MACHINE LEARNING CLASS Overlaping Imbalanced Datases 

分 类 号：R73[医药卫生—肿瘤]