Data Labeling with Novel Decision Module of Tri-training

Tseng, Chuan-Mu; Huang, Tzu-Wen; Liu, Tzong-Jye

doi:10.1109/iccci49374.2020.9145968

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…The experimental results show that the “teacher‐student” model proposed by Liu et al is superior to tri‐training and self‐training algorithms and achieves the optimal results quickly by using of fewer labeled samples. Tseng et al 52 proposed the NDMTT algorithm by adding a confidence threshold mechanism based on tri‐training. On the basis of the voting mechanism, the pseudo‐labels of the unlabeled data are further screened, so that the accuracy of confidence estimation is further improved.…”

Section: The Main Steps Of Co‐trainingmentioning

confidence: 99%

“…Experiments on the LAPR TC‐2 and NUS‐WIDE datasets show that the accuracy, recall, F‐measure, N+ and mAP are better than other methods.Joint training is based on the voting results of two algorithms to obtain labeling results, but when the two algorithms produce inconsistent results, the samples will not be labeled correctly. To solve this problem, Tseng et al 52 proposed the novel decision module of tri‐training (NDMTT) method to improve the automatic data annotation process based on co‐training. By adding a third algorithm to assist in judging the credibility of pseudo‐labeled samples, the effectiveness of labeled data is improved.…”

Section: Application Of Co‐trainingmentioning

confidence: 99%

“…when the two algorithms produce inconsistent results, the samples will not be labeled correctly. To solve this problem, Tseng et al 52 proposed the novel decision module of tri-training (NDMTT) method to improve the automatic data annotation process based on co-training. By adding a third algorithm to assist in judging the credibility of pseudo-labeled samples, the effectiveness of labeled data is improved.…”

Section: Application Of Co-trainingmentioning

confidence: 99%

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A review of research on co‐training

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Wang

et al. 2021

Concurrency and Computation

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Co-training algorithm is one of the main methods of semi-supervised learning in machine learning, which explores the effective information in unlabeled data by multi-learner collaboration. Based on the development of co-training algorithm, the research work in recent years was further summarized in this article. In particular, three main steps of relevant co-training algorithms are introduced: view acquisition, learners' differentiation, and label confidence estimation. Finally, we summarized the problems existing in the current co-training methods, gave some suggestions for improvement, and looked forward to the future development direction of the co-training algorithm.

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Section: The Main Steps Of Co‐trainingmentioning

confidence: 99%

Section: Application Of Co‐trainingmentioning

confidence: 99%

Section: Application Of Co-trainingmentioning

confidence: 99%

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A review of research on co‐training

Ning

Wang

et al. 2021

Concurrency and Computation

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Semi-Supervised Gaussian Processes Active Learning Model for Imbalanced Small Data Based on Tri-Training With Data Enhancement

Zhou

Zou

2023

IEEE Access

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To solve the problem that some imbalanced small sample datasets only contain a few labeled samples, a semi-supervised gaussian processes active learning model based on improved tri-training with enhanced data is proposed. Firstly, the label samples are balanced and enhanced, and we present a quantitative enhanced data evaluation criteria based on the JS distance and the similarity of information entropy between enhanced data and original data to select the best enhanced data. Secondly, an improved semi-supervised learning method based on tri-training is proposed to find the unlabeled samples which have high confidence, so the certainty of the labeled samples group can be increased, in order to ensure that the three classifiers of tri-training have both difference and robustness, random forest is introduced to divide the features of the dataset into three groups with equal contribution, and each classifier trains different combinations of two feature groups. Thirdly, in order to query and classify the most informative unlabeled samples more precisely, active learning based on the Gaussian process and JS distribution range is structured, because of the high uncertainty of the unlabeled samples predicted by active learning, the similarity distribution range of JS distance is introduced to compare the similarity of unlabeled samples and labeled samples in active learning`s classifier, so the model can classify more diverse samples. The final experimental results show that compared with several traditional models, the proposed model performs better on artificial datasets and imbalanced small-size UCI datasets.INDEX TERMS active learning, imbalaned small dataset, Gaussian procession, semi-supervised learning, tri-training

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An Improved Co-Training and Generative Adversarial Network (Diff-CoGAN) for Semi-Supervised Medical Image Segmentation

Jamil

Hamzah

2023

Information

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Semi-supervised learning is a technique that utilizes a limited set of labeled data and a large amount of unlabeled data to overcome the challenges of obtaining a perfect dataset in deep learning, especially in medical image segmentation. The accuracy of the predicted labels for the unlabeled data is a critical factor that affects the training performance, thus reducing the accuracy of segmentation. To address this issue, a semi-supervised learning method based on the Diff-CoGAN framework was proposed, which incorporates co-training and generative adversarial network (GAN) strategies. The proposed Diff-CoGAN framework employs two generators and one discriminator. The generators work together by providing mutual information guidance to produce predicted maps that are more accurate and closer to the ground truth. To further improve segmentation accuracy, the predicted maps are subjected to an intersection operation to identify a high-confidence region of interest, which reduces boundary segmentation errors. The predicted maps are then fed into the discriminator, and the iterative process of adversarial training enhances the generators’ ability to generate more precise maps, while also improving the discriminator’s ability to distinguish between the predicted maps and the ground truth. This study conducted experiments on the Hippocampus and Spleen images from the Medical Segmentation Decathlon (MSD) dataset using three semi-supervised methods: co-training, semi-GAN, and Diff-CoGAN. The experimental results demonstrated that the proposed Diff-CoGAN approach significantly enhanced segmentation accuracy compared to the other two methods by benefiting on the mutual guidance of the two generators and the adversarial training between the generators and discriminator. The introduction of the intersection operation prior to the discriminator also further reduced boundary segmentation errors.

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Data Labeling with Novel Decision Module of Tri-training

Cited by 3 publications

References 17 publications

A review of research on co‐training

A review of research on co‐training

Semi-Supervised Gaussian Processes Active Learning Model for Imbalanced Small Data Based on Tri-Training With Data Enhancement

An Improved Co-Training and Generative Adversarial Network (Diff-CoGAN) for Semi-Supervised Medical Image Segmentation

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