计算机科学与探索2018，Vol.12Issue(7)：1145-1153,9.DOI:10.3778/j.issn.1673-9418.1705080
绝对不平衡样本分类的集成迁移学习算法
Ensemble Transfer Learning Algorithm for Absolute Imbalanced Data Classification
摘要
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 irre-versible in the TrAdaBoost algorithm, the weight recovery factor is introduced. At the data selection stage, the algo-rithm 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 do-main. 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.关键词
集成迁移学习/级联模型/不平衡数据/TrAdaBoostKey words
ensemble transfer learning/cascade module/imbalanced data/TrAdaBoost分类
信息技术与安全科学引用本文复制引用
么素素,王宝亮,侯永宏..绝对不平衡样本分类的集成迁移学习算法[J].计算机科学与探索,2018,12(7):1145-1153,9.基金项目
The National Natural Science Foundation of China under Grant No. 61571325(国家自然科学基金). （国家自然科学基金）
