Cost-Sensitive Large margin Distribution Machine for classification of imbalanced data

• Published in: Pattern recognition letters 2016-09, Vol.80, p.107-112
• Main Authors: Cheng, Fanyong, Zhang, Jing, Wen, Cuihong
• Format: Article
• Language: English
• Subjects: Balanced detection rate; Cost-sensitive learning; Imbalanced training data; Margin distribution; Minimum margin
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2016.06.009

•Large margin Distribution Machine (LDM) is not satisfactory on imbalanced training data.•Cost-sensitive margin distribution is introduced to design a balanced classifier.•Cost-sensitive LDM (CS-LDM) has a very strong generalization performance.•CS-LDM can gradually improve the detection rate of the minority class.•CS-LDM can obtain a balanced detection rate at the balance point. This paper proposes a new method to design a balanced classifier on imbalanced training data based on margin distribution theory. Recently, Large margin Distribution Machine (LDM) is put forward and it obtains superior classification performance compared with Support Vector Machine (SVM) and many state-of-the-art methods. However, one of the deficiencies of LDM is that it easily leads to the lower detection rate of the minority class than that of the majority class on imbalanced data which contradicts to the needs of high detection rate of the minority class in the real application. In this paper, Cost-Sensitive Large margin Distribution Machine (CS-LDM) is brought forward to improve the detection rate of the minority class by introducing cost-sensitive margin mean and cost-sensitive penalty. Theoretical and experimental results show that CS-LDM can gradually improve the detection rate of the minority class with the increasing of the cost parameter and obtain a balanced classifier when the cost parameter increases to a certain value. CS-LDM is superior to some popular cost-sensitive methods and can be used in many applications.