A multi-source transfer-based decision-making method with domain consistency and contributions

BackgroundAutomatic extraction of biomedical events from literature, that allows for faster update of the latest discoveries automatically, is a heated research topic now. Trigger word recognition is a critical step in the process of event extraction. Its performance directly influences the results of the event extraction. In general, machine learning-based trigger recognition approaches such as neural networks must to be trained on a dataset with plentiful annotations to achieve high performances. However, the problem of the datasets in wide coverage event domains is that their annotations are insufficient and imbalance. One of the methods widely used to deal with this problem is transfer learning. In this work, we aim to extend the transfer learning to utilize multiple source domains. Multiple source domain datasets can be jointly trained to help achieve a higher recognition performance on a target domain with wide coverage events.ResultsBased on the study of previous work, we propose an improved multi-source domain neural network transfer learning architecture and a training approach for biomedical trigger detection task, which can share knowledge between the multi-source and target domains more comprehensively. We extend the ability of traditional adversarial networks to extract common features between source and target domains, when there is more than one dataset in the source domains. Multiple feature extraction channels to simultaneously capture global and local common features are designed. Moreover, under the constraint of an extra classifier, the multiple local common feature sub-channels can extract and transfer more diverse common features from the related multi-source domains effectively. In the experiments, MLEE corpus is used to train and test the proposed model to recognize the wide coverage triggers as a target dataset. Other four corpora with the varying degrees of relevance with MLEE from different domains are used as source datasets, respectively. Our proposed approach achieves recognition improvement compared with traditional adversarial networks. Moreover, its performance is competitive compared with the results of other leading systems on the same MLEE corpus.ConclusionsThe proposed Multi-Source Transfer Learning-based Trigger Recognizer (MSTLTR) can further improve the performance compared with the traditional method, when the source domains are more than one. The most essential improvement is that our approach represents common features in two aspects: the global common features and the local common features. Hence, these more sharable features improve the performance and generalization of the model on the target domain effectively.

Multi-source partial domain adaptation method based on pseudo-balanced target domain for fault diagnosis

Objective: Electroencephalogram (EEG) based motor imagery brain–computer interface (MI BCI) has exciting prospects in applications. Multi-source domain problem of MI EEG decoding needs to be solved urgently. That is, how to use existing vast amounts of MI EEG data (multi-source domain) to train inter-device algorithms for new equipment (target domain) decoding. Methods: In this work, we propose a compact SRENet method and a sample reweighting training strategy to solve this issue. The target domain is expressed as a weighted combination of multi-source domains to improve the decoding performance of inter-device MI. A novel sample reweighting classifier and a conditional reweighting discriminator are used for reweighting multi-source domain samples in training process. Results: We evaluated the performance of SRENet on three public datasets. The results outperformed baseline method by 6.88%, 5.90% and 3.49% on the three tasks, respectively. Conclusion: Experimental results verified the effectiveness of the proposed method for multi-source domain problems. The inter-device MI performance has been significantly improved. Significance: This study provides a new solution for multi-source domain problem in MI EEG decoding, which will make better use of existing EEG datasets and help people use BCI more easily.

For transfer learning, many research works have demonstrated that effective use of information from multi-source domains will improve classification performance. In this paper, we propose a method of Targetize Multi-source Domain Bridged by Common Subspace (TMSD) for face recognition, which transfers rich supervision knowledge from more than one labeled source domains to the unlabeled target domain. Specifically, a common subspace is learnt for several domains by keeping the maximum total correlation. In this way, the discrepancy of each domain is reduced, and the structures of both the source and target domains are well preserved for classification. In the common subspace, each sample projected from the source domains is sparsely represented as a linear combination of several samples projected from the target domain, such that the samples projected from different domains can be well interlaced. Then, in the original image space, each source domain image can be represented as a linear combination of neighbors in the target domain. Finally, the discriminant subspace can be obtained by targetized multi-source domain images using supervised learning algorithm. The experimental results illustrate the superiority of TMSD over those competitive ones.

Brain-computer interfaces (BCIs) have been widely focused and extensively studied in recent years for their huge prospect of medical rehabilitation and commercial applications. Transfer learning exploits the information in the source domain and applies in another different but related domain (target domain), and is therefore introduced into the BCIs to figure out the inter-subject variances of electroencephalography (EEG) signals. In this article, a novel transfer learning method is proposed to preserve the Riemannian locality of data structure in both the source and target domains and simultaneously realize the joint distribution adaptation of both domains to enhance the effectiveness of transfer learning. Specifically, a Riemannian graph is first defined and constructed based on the Riemannian distance to represent the Riemannian geometry information. To simultaneously align the marginal and conditional distribution of source and target domains and preserve the Riemannian locality of data structure in both domains, the Riemannian graph is embedded in the joint distribution adaptation (JDA) framework and forms the proposed Riemannian locality preserving-based transfer learning (RLPTL). To validate the effect of the proposed method, it is compared with several existing methods on two open motor imagery datasets, and both multi-source domains (MSD) and single-source domains (SSD) experiments are considered. Experimental results show that the proposed method achieves the highest accuracies in MSD and SSD experiments on three datasets and outperforms eight baseline methods, which demonstrates that the proposed method creates a feasible and efficient way to realize transfer learning.

Multi-source domain transfer learning with small sample learning for thermal runaway diagnosis of lithium-ion battery

Domain adaptation, as an important branch of transfer learning, can be applied to cope with data insufficiency and high subject variabilities in motor imagery electroencephalogram (MI-EEG) based brain-computer interfaces. The existing methods generally focus on aligning data and feature distribution; however, aligning each source domain with the informative samples of the target domain and seeking the most appropriate source domains to enhance the classification effect has not been considered. In this paper, we propose a dual alignment-based multi-source domain adaptation framework, denoted DAMSDAF. Based on continuous wavelet transform, all channels of MI-EEG signals are converted respectively and the generated time-frequency spectrum images are stitched to construct multi-source domains and target domain. Then, the informative samples close to the decision boundary are found in the target domain by using entropy, and they are employed to align and reassign each source domain with normalized mutual information. Furthermore, a multi-branch deep network (MBDN) is designed, and the maximum mean discrepancy is embedded in each branch to realign the specific feature distribution. Each branch is separately trained by an aligned source domain, and all the single branch transfer accuracies are arranged in descending order and utilized for weighted prediction of MBDN. Therefore, the most suitable number of source domains with top weights can be automatically determined. Extensive experiments are conducted based on 3 public MI-EEG datasets. DAMSDAF achieves the classification accuracies of 92.56%, 69.45% and 89.57%, and the statistical analysis is performed by the kappa value and t-test. Experimental results show that DAMSDAF significantly improves the transfer effects compared to the present methods, indicating that dual alignment can sufficiently use the different weighted samples and even source domains at different levels as well as realizing optimal selection of multi-source domains.

A knowledge dynamic matching unit-guided multi-source domain adaptation network with attention mechanism for rolling bearing fault diagnosis

Domain adaptation has been widely used in fault diagnosis and dealt with data distribution discrepancies, but the labels in source and target domains are usually assumed to be identical. The complexity of the working conditions, in reality, leads to the fact that the labels in target domains are often a subset of the labels in source domains. This special case is called the partial domain problem. However, most of the existing proposed methods for solving partial domain problems are limited to single-source-domain scenarios and fail to effectively integrate multisource knowledge. Hence, this study proposes a new approach of multisource domain obfuscation-subdomain alignment (MSDO-SA) for partial domain adaptation fault diagnosis in multisource domains. Through domain obfuscation, the multisource domains are converted into a single source domain. The subdomain alignment aims at improving the generalization ability and relevance of the model, and effectively alleviates the domain shift problem. Finally, multiple partial domain fault diagnosis tasks using the CWRU dataset validate the effectiveness, robustness, and superiority of the proposed method.

Social media is a real-time social sensor to sense and collect diverse information, which can be combined with sentiment analysis to help IoT sensors provide user-demanded favorable data in smart systems. In the case of insufficient data labels, cross-domain sentiment analysis aims to transfer knowledge from the source domain with rich labels to the target domain that lacks labels. Most domain adaptation sentiment analysis methods achieve transfer learning by reducing the domain differences between the source and target domains, but little attention is paid to the negative transfer problem caused by invalid source domains. To address these problems, this paper proposes a cross-domain sentiment analysis method based on feature projection and multi-source attention (FPMA), which not only alleviates the effect of negative transfer through a multi-source selection strategy but also improves the classification performance in terms of feature representation. Specifically, two feature extractors and a domain discriminator are employed to extract shared and private features through adversarial training. The extracted features are optimized by orthogonal projection to help train the classification in multi-source domains. Finally, each text in the target domain is fed into the trained module. The sentiment tendency is predicted in the weighted form of the attention mechanism based on the classification results from the multi-source domains. The experimental results on two commonly used datasets showed that FPMA outperformed baseline models.

Nowadays large volumes of industrial data are being actively generated and collected in various power system applications. Industrial Analytics in the power system field requires more powerful and intelligent machine learning tools, strategies, and environments to properly analyze the historical data and extract predictive knowledge. This paper discusses the situation and limitations of current approaches, analytic models, and tools utilized to conduct predictive machine learning analytics for very large volumes of data where the data processing causes the processor to run out of memory. Two industrial analytics cases in the power systems field are presented. Our results indicated the feasibility of forecasting substations fault events and power load using machine learning algorithm written in MapReduce paradigm or machine learning tools specific for Big Data.

A novel multi-morphological representation approach for multi-source EEG signals

Conditional distribution-guided adversarial transfer learning network with multi-source domains for rolling bearing fault diagnosis

Multi-source domain adaptation (MSDA) aims to transfer knowledge from multi-source domains to one target domain. Inspired by single-source domain adaptation, existing methods solve MSDA by aligning the data distributions between the target domain and each source domain. However, aligning the target domain with the dissimilar source domain would harm the representation learning. To address the above issue, an intuitive motivation of MSDA is using the attention mechanism to enhance the positive effects of the similar domains, and suppress the negative effects of the dissimilar domains. Therefore, we propose Attention-Based Multi-Source Domain Adaptation (ABMSDA) by considering the domain correlations to alleviate the effects caused by dissimilar domains. To obtain the domain correlations between source and target domains, ABMSDA firstly trains a domain recognition model to calculate the probability that the target images belong to each source domain. Based on the domain correlations, Weighted Moment Distance (WMD) is proposed to pay more attention on the source domains with higher similarities. Furthermore, Attentive Classification Loss (ACL) is developed to constrain that the feature extractor can generate the alignment and discriminative visual representations. The evaluations on two benchmarks demonstrate the effectiveness of the proposed model, e.g., an average of 6.1% improvement on the challenging DomainNet dataset.

Accurate crop mapping is critical for agricultural applications. Although studies have combined deep learning methods and time-series satellite images to crop classification with satisfactory results, most of them focused on supervised methods, which are usually applicable to a specific domain and lose their validity in new domains. Unsupervised domain adaptation (UDA) was proposed to solve this limitation by transferring knowledge from source domains with labeled samples to target domains with unlabeled samples. Particularly, multi-source UDA (MUDA) is a powerful extension that leverages knowledge from multiple source domains and can achieve better results in the target domain than single-source UDA (SUDA). However, few studies have explored the potential of MUDA for crop mapping. This study proposed a MUDA crop classification model (MUCCM) for unsupervised crop mapping. Specifically, 11 states in the U.S. were selected as the multi-source domains, and three provinces in Northeast China were selected as individual target domains. Ten spectral bands and five vegetation indexes were collected at a 10-day interval from time-series Sentinel-2 images to build the MUCCM. Subsequently, a SUDA model Domain Adversarial Neural Network (DANN) and two direct transfer methods, namely, the deep neural network and random forest, were constructed and compared with the MUCCM. The results indicated that the UDA models outperformed the direct transfer models significantly, and the MUCCM was superior to the DANN, achieving the highest classification accuracy (OA>85%) in each target domain. In addition, the MUCCM also performed best in in-season forecasting and crop mapping. This study is the first to apply a MUDA to crop classification and demonstrate a novel, effective solution for high-performance crop mapping in regions without labeled samples.