Multi-Source Tri-Training Transfer Learning

Cheng, Yuhu; Wang, Xuesong; Cao, Ge

doi:10.1587/transinf.e97.d.1668

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…It expands the AdaBoost algorithm and applies boosting technology to weigh the source and the target samples. Many algorithms are put forward to extend TrAdaBoost, such as DTrAdaBoost [ 43 ], Multisource-TrAdaboost (MTrA), and Task-TrAdaboost (TTrA) [ 44 ], Multi-Source Tri-Training Transfer Learning (MST3L) [ 45 ].…”

Section: Related Workmentioning

confidence: 99%

Transfer Extreme Learning Machine with Output Weight Alignment

Zang

Cheng

Wang

et al. 2021

Computational Intelligence and Neuroscience

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Extreme Learning Machine (ELM) as a fast and efficient neural network model in pattern recognition and machine learning will decline when the labeled training sample is insufficient. Transfer learning helps the target task to learn a reliable model by using plentiful labeled samples from the different but relevant domain. In this paper, we propose a supervised Extreme Learning Machine with knowledge transferability, called Transfer Extreme Learning Machine with Output Weight Alignment (TELM-OWA). Firstly, it reduces the distribution difference between domains by aligning the output weight matrix of the ELM trained by the labeled samples from the source and target domains. Secondly, the approximation between the interdomain ELM output weight matrix is added to the objective function to further realize the cross-domain transfer of knowledge. Thirdly, we consider the objective function as the least square problem and transform it into a standard ELM model to be efficiently solved. Finally, the effectiveness of the proposed algorithm is verified by classification experiments on 16 sets of image datasets and 6 sets of text datasets, and the result demonstrates the competitive performance of our method with respect to other ELM models and transfer learning approach.

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Section: Related Workmentioning

confidence: 99%

Transfer Extreme Learning Machine with Output Weight Alignment

Zang

Cheng

Wang

et al. 2021

Computational Intelligence and Neuroscience

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“…In COITL, two base learners are trained on the target domain data, and each learner is refined using the weighted source domain examples predicted by the other. In addition, a number of other instance-based inductive transfer methods have been proposed to extend single source domain to multiple source domains (Cheng et al, 2014;Ding et al, 2016;Yao & Doretto, 2010;Yang et al, 2020).…”

Section: Instance-based Transfer Learningmentioning

confidence: 99%

A new semi-supervised inductive transfer learning framework: Co-Transfer

Yuan,

Wen

2021

Preprint

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In many practical data mining scenarios, such as network intrusion detection, Twitter spam detection, and computer-aided diagnosis, a source domain that is different from but related to a target domain is very common. In addition, a large amount of unlabeled data is available in both source and target domains, but labeling each of them is difficult, expensive, time-consuming, and sometime unnecessary. Therefore, it is very important and worthwhile to fully explore the labeled and unlabeled data in source and target domains to settle the task in target domain. In this paper, a new semi-supervised inductive transfer learning framework, named Co-Transfer is proposed. Co-Transfer first generates three TrAdaBoost classifiers for transfer learning from the source domain to the target domain, and meanwhile another three TrAdaBoost classifiers are generated for transfer learning from the target domain to the source domain, using bootstraped samples from the original labeled data. In each round of co-transfer, each group of TrAdaBoost classifiers are refined using the carefully labeled data. Finally, the group of TrAdaBoost classifiers learned to transfer from the source domain to the target domain produce the final hypothesis. Experiments results illustrate Co-Transfer can effectively exploit and reuse the labeled and unlabeled data in source and target domains.Preprint. Under review.

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“…To further demonstrate the performance of METL, we compare it with three transfer learning algorithms: MultiSource-TrAdaBoost (MTrA) [13], Multi-Source Dynamic TrAdaBoost (MSDTrA) [14], and Multi-Source Tri-Training Transfer Learning (MST 3 L) [36]. The main settings of algorithms are shown in TABLE 4.…”

Section: ) Comparison With Existing Approachesmentioning

confidence: 99%

Multi-Source Transfer Learning via Ensemble Approach for Initial Diagnosis of Alzheimer’s Disease

Yang

Wang

et al. 2020

IEEE J. Transl. Eng. Health Med.

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Alzheimer's disease (AD) is one of the most common progressive neurodegenerative diseases, and the number of AD patients has increased year after year with the global aging trend. The onset of AD has a long preclinical stage. If doctors can make an initial diagnosis in the mild cognitive impairment (MCI) stage, it is possible to identify and screen those at a high-risk of developing full-blown AD, and thus the number of new AD patients can be reduced. However, there are problems with the medical datasets including AD data, such as insufficient number of samples and different data distributions. Transfer learning, which can effectively solve the problem of distribution discrepancy between training and test data and an insufficient number of target samples, has attracted increasing attention over recent years. In this paper, we propose a multi-source ensemble transfer learning (METL) approach by introducing ensemble learning and our tritransfer model that uses Tri-Training, which ensures the transferability of source data by the tri-transfer model and high performance through ensemble learning. The experimental results on the benchmark and AD datasets demonstrate that our proposed approach has effective transferability, robustness, and feasibility, and is superior to existing algorithms. Based on METL, we propose an auxiliary diagnosis system for the initial diagnosis of AD, which helps doctors identify patients in the MCI stage as quickly as possible and with high accuracy so that measures can be taken to prevent or delay the occurrence of AD.

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Multi-Source Tri-Training Transfer Learning

Cited by 12 publications

References 7 publications

Transfer Extreme Learning Machine with Output Weight Alignment

Transfer Extreme Learning Machine with Output Weight Alignment

A new semi-supervised inductive transfer learning framework: Co-Transfer

Multi-Source Transfer Learning via Ensemble Approach for Initial Diagnosis of Alzheimer’s Disease

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