Ying Lv

Domain adaptation aims to build up a learning model for target domain by leveraging transferable knowledge from different but related source domains. Existing domain adaptation methods generally transfer the knowledge from source domain to target domain through measuring the consistency between the different domains. Under this strategy, if the data of source domain is not sufficient to guarantee the consistency, the transferable knowledge will be very limited. On the other hand, we often have priors about target domain which facilitate knowledge transfer but are neglected in the extant domain adaptation methods. To tackle the problems, we integrate the priors of target domain into transfer process and propose a domain adaptation method based on evidence evidence theory. We represent the priors with evidential belief function and reformulate the domain adaptation objective based on likelihood principle, in which the priors are used to adjust transferred knowledge to suit for target domain. Based on this, we propose an improved coordinate ascent algorithm to optimize likelihood objective of domain adaption. Experimental results on both text and image datasets validate that the proposed method is effective to improve the knowledge transferability in domain adaptation, especially when the source domain is limited.

T/R components are important components in radar, and the analysis of the temperature field of T/R components is a key problem in radar. In order to study the thermal effect of T/R components, ANSYS Workbench finite element thermal analysis is introduced to analyze and design the heat transfer. Firstly, the actual operating temperature is read for testing and a three-dimensional model of the T/R component is established. In addition to considering the thermal effect of the chip and the three-dimensional structure of the component, the model also considers the material properties of different locations of the component. Secondly, the temperature field distribution of the T/R component is obtained by simulation analysis, and finally the simulation results are compared with the actual measurement results, and it is found that the error between the simulation results and the measurement results is less than 10%, which provides a basis for the reliability of the component thermal design.

Transfer learning (TL) hopes to train a model for target domain tasks by using knowledge from different but related source domains. Most TL methods focus more on improving the predictive performance of the single model across domains. Since domain differences cannot be avoided, the knowledge from the source domain to obtain the target domain is limited. Therefore, the transfer model has to predict out-of-distribution (OOD) data in the target domain. However, the prediction of the single model is unstable when dealing with the OOD data, which can easily cause negative transfer. To solve this problem, we propose a parallel ensemble strategy based on Determinantal Point Processes (DPP) for transfer learning. In this strategy, we first proposed an improved DPP sampling to generate training subsets with higher transferability and diversity. Second, we use the subsets to train the base models. Finally, the base models are fused using the adaptability of subsets. To validate the effectiveness of the ensemble strategy, we couple the ensemble strategy into traditional TL models and deep TL models and evaluate the transfer performance models on text and image data sets. The experiment results show that our proposed ensemble strategy can significantly improve the performance of the transfer model.

Domain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to the target domain task. Ignoring the uncertainty may lead to models with unreliable and suboptimal classification results for the target domain task. However, most previous works focus on reducing the gap in data distribution between the source and target domains. They do not consider the uncertainty of source domain data about the target domain task and cannot apply the uncertainty to learn an adaptive classifier. Aimed at this problem, we revisit the domain adaptation from source domain data uncertainty based on evidence theory and thereby devise an adaptive classifier with the uncertainty measure. Based on evidence theory, we first design an evidence net to estimate the uncertainty of source domain data about the target domain task. Second, we design a general loss function with the uncertainty measure for the adaptive classifier and extend the loss function to support vector machine. Finally, numerical experiments on simulation datasets and real-world applications are given to comprehensively demonstrate the effectiveness of the adaptive classifier with the uncertainty measure.

Bladder cancer is a malignant disease with substantial morbidity and mortality. Bladder cancer staging is crucial to determine the effective treatments of bladder tumors in clinic. As to the superiority of feature learning, Deep Convolutional Neural Networks (DCNN) are widely used to predict the cancer stage based on medical images. However, most existing DCNN-based cancer staging methods are data-driven and neglect the domain knowledge and experiences of clinicians. Besides, the deep neural networks are short of model interpretability and may lead to risky diagnosis. To tackle the problems, we construct the diagnosis rules of bladder cancer staging based on the clinical experiences of tumor penetration into bladder wall. The diagnosis rules are extracted from Magnetic Resonance (MR) images and further integrated into DCNN for joint identification of tumor stage. The experiments validate that the integrated rules improve the model interpretability and guide DCNN to focus on the regions of tumor penetration and thereby produce precise prediction of cancer staging.

A large-scale and high-quality training dataset is an important guarantee to learn an ideal classifier for text sentiment classification. However, manually constructing such a training dataset with sentiment labels is a labor-intensive and time-consuming task. Therefore, based on the idea of effectively utilizing unlabeled samples, a synthetical framework that covers the whole process of semi-supervised learning from seed selection, iterative modification of the training text set, to the co-training strategy of the classifier is proposed in this paper for text sentiment classification. To provide an important basis for selecting the seed texts and modifying the training text set, three kinds of measures—the cluster similarity degree of an unlabeled text, the cluster uncertainty degree of a pseudo-label text to a learner, and the reliability degree of a pseudo-label text to a learner—are defined. With these measures, a seed selection method based on Random Swap clustering, a hybrid modification method of the training text set based on active learning and self-learning, and an alternately co-training strategy of the ensemble classifier of the Maximum Entropy and Support Vector Machine are proposed and combined into our framework. The experimental results on three Chinese datasets (COAE2014, COAE2015, and a Hotel review, respectively) and five English datasets (Books, DVD, Electronics, Kitchen, and MR, respectively) in the real world verify the effectiveness of the proposed framework.