[1]ZHOU Jingyu,WANG Shitong.Multi-source online transfer learning for imbalanced target domains[J].CAAI Transactions on Intelligent Systems,2022,17(2):248-256.[doi:10.11992/tis.202012019]
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Multi-source online transfer learning for imbalanced target domains

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 2 Page number: 248-256 Column: 学术论文—机器学习 Public date: 2022-03-05
Title:
Multi-source online transfer learning for imbalanced target domains
Author(s):
ZHOU Jingyu;  WANG Shitong
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Keywords:
multi-source transfer learning; online learning; target domain; imbalanced data; oversampling; k-nearest neighbor; input space; feature space
CLC:
TP181
DOI:
10.11992/tis.202012019
Abstract:
Multi-source online transfer learning has been widely used in applications where the relevant source domain contains a large amount of labeled data and the data in the target domain is achieved in the form of data flow. However, the class distribution of the target domain is sometimes imbalanced. Aiming at the unbalanced binary classification problem wherein the target domain reaches multiple data online at a time, this paper proposes a multi-source online transfer learning algorithm by means of oversampling the target domain samples. First, the algorithm finds the k-nearest neighbors of the current batch of samples from the previous batch, then generates a small number of majority class samples, finally generating a minority class to balance the class distribution of the current batch of samples. Each batch of synthetic and real samples train the target domain function together, thereby improving the classification performance of the target domain function. At the same time, methods for oversampling in the input space and feature space of the target domain are designed respectively, and comprehensive experiments are conducted on multiple real-world data sets to prove the effectiveness of the proposed algorithm.
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