標題: Model-Based Synthetic Sampling for Imbalanced Data
作者: Liu, Chien-Liang
Hsieh, Po-Yen
工業工程與管理學系
Department of Industrial Engineering and Management
關鍵字: Data models;Machine learning;Training;Sampling methods;Manufacturing;Kernel;Data mining;Imbalanced data;over-sampling;synthetic sampling;model-based approach
公開日期: 1-Aug-2020
摘要: Imbalanced data is characterized by the severe difference in observation frequency between classes and has received a lot of attention in data mining research. The prediction performances usually deteriorate as classifiers learn from imbalanced data, as most classifiers assume the class distribution is balanced or the costs for different types of classification errors are equal. Although several methods have been devised to deal with imbalance problems, it is still difficult to generalize those methods to achieve stable improvement in most cases. In this study, we propose a novel framework called model-based synthetic sampling (MBS) to cope with imbalance problems, in which we integrate modeling and sampling techniques to generate synthetic data. The key idea behind the proposed method is to use regression models to capture the relationship between features and to consider data diversity in the process of data generation. We conduct experiments on 13 datasets and compare the proposed method with 10 methods. The experimental results indicate that the proposed method is not only comparative but also stable. We also provide detailed investigations and visualizations of the proposed method to empirically demonstrate why it could generate good data samples.
URI: http://dx.doi.org/10.1109/TKDE.2019.2905559
http://hdl.handle.net/11536/154864
ISSN: 1041-4347
DOI: 10.1109/TKDE.2019.2905559
期刊: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume: 32
Issue: 8
起始頁: 1543
結束頁: 1556
Appears in Collections:Articles