• 人工智能与模式识别 •

### 绝对不平衡样本分类的集成迁移学习算法

1. 1. 天津大学 电气自动化与信息工程学院，天津 300072
2. 天津大学 信息与网络中心，天津 300072
• 出版日期:2018-07-01 发布日期:2018-07-06

### Ensemble Transfer Learning Algorithm for Absolute Imbalanced Data Classification

YAO Susu, WANG Baoliang, HOU Yonghong

1. 1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
2. Information and Network Center, Tianjin University, Tianjin 300072, China
• Online:2018-07-01 Published:2018-07-06

Abstract:

According to the problem of mining with absolute imbalanced data, this paper proposes an ensemble transfer learning algorithm based on cascade structure. The algorithm consists of two parts: the transfer learning and the data selection. At the transfer learning stage, to solve the problem that the weight of auxiliary domain data is irreversible in the TrAdaBoost algorithm, the weight recovery factor is introduced. At the data selection stage, the algorithm gradually deletes the noise samples and redundant samples of the auxiliary domain at each node of cascade structure. The algorithm makes full use of the auxiliary domain data while ensuring the leading role of the target domain. The experimental results on the real data sets show that the algorithm has better effect on the [F-measure] value and [G-mean] value under the condition of absolute imbalanced data. Therefore, the proposed algorithm can solve the problem of absolute imbalance of training data to a certain extent.