关键词: 集成迁移学习, 级联模型, 不平衡数据, TrAdaBoost

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.

Key words: ensemble transfer learning, cascade module, imbalanced data, TrAdaBoost