Boosting imbalanced data learning with Wiener process oversampling

• Published in: Frontiers of Computer Science 2017-10, Vol.11 (5), p.836-851
• Main Authors: LI, Qian, LI, Gang, NIU, Wenjia, CAO, Yanan, CHANG, Liang, TAN, Jianlong, GUO, Li
• Format: Article
• Language: English
• Subjects: AdaBoost; Algorithms; Computer Science; Data mining; ensemble learning; imbalanced-data learning; Machine learning; Normal distribution; Oversampling; Research Article; Synthesis; Wiener process; Wiener过程; 不平衡数据; 信息需要; 机器学习; 正态分布; 维纳过程; 训练样本; 过采样
• ISSN: 2095-2228; 2095-2236
• DOI: 10.1007/s11704-016-5250-y

Learning from imbalanced data is a challenging task in a wide range of applications, which attracts significant research efforts from machine learning and data mining community. As a natural approach to this issue, oversampling balances the training samples through replicating existing samples or synthesizing new samples. In general, synthesization outperforms replication by supplying additional information on the minority class. However, the additional information needs to follow the same normal distribution of the training set, which further constrains the new samples within the predefined range of training set. In this paper, we present the Wiener process oversampling （WPO） technique that brings the physics phenomena into sample synthesization. WPO constructs a robust decision region by expanding the attribute ranges in training set while keeping the same normal distribution. The satisfactory performance of WPO can be achieved with much lower computing complexity. In addition, by integrating WPO with ensemble learning, the WPOBoost algorithm outperforms many prevalent imbalance learning solutions.