Self-training semi-supervised classification based on density peaks of data

Wu, Di; Shang, Ming; Luo, Xin; Ji, Xu; Yan, Hu; Deng, Wei; Wang, Guoyin

doi:10.1016/j.neucom.2017.05.072

Cited by 100 publications

(47 citation statements)

References 30 publications

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“…Moreover, self-training, a type of semi-supervised learning, to learn by gradually including high-to low-confidence samples as pseudo-labelled samples has been proposed [39]. Self-training has been successfully applied to computer vision [40], data density peaks [41], computed tomography (CT) colonography [42] and other fields. In this paper, self-training with deep forest as base learners is used to learn from both labelled and unlabelled instances; in particular, the experiments shows that an ensemble learner provides additional improvement over the performance of adapted learners [43].…”

Section: Literature Reviewmentioning

confidence: 99%

Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning

Yin

Chen

2020

Healthcare

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Disease classification based on machine learning has become a crucial research topic in the fields of genetics and molecular biology. Generally, disease classification involves a supervised learning style; i.e., it requires a large number of labelled samples to achieve good classification performance. However, in the majority of the cases, labelled samples are hard to obtain, so the amount of training data are limited. However, many unclassified (unlabelled) sequences have been deposited in public databases, which may help the training procedure. This method is called semi-supervised learning and is very useful in many applications. Self-training can be implemented using high- to low-confidence samples to prevent noisy samples from affecting the robustness of semi-supervised learning in the training process. The deep forest method with the hyperparameter settings used in this paper can achieve excellent performance. Therefore, in this work, we propose a novel combined deep learning model and semi-supervised learning with self-training approach to improve the performance in disease classification, which utilizes unlabelled samples to update a mechanism designed to increase the number of high-confidence pseudo-labelled samples. The experimental results show that our proposed model can achieve good performance in disease classification and disease-causing gene identification.

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Section: Literature Reviewmentioning

confidence: 99%

Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning

Yin

Chen

2020

Healthcare

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“…Many semi-supervised learning methods were proposed in the literature [159], e.g. self-training [160], [161], co-training [162], [163], expectation-maximization (EM) [164], [165] and graph based methods [166], [167]. Among these methods, co-training was theoretically proved to be very appropriate and successful in combining the labeled and unlabeled data under three strong assumptions in [168].…”

Section: Semi-supervised Learning Based Mmcnns For Rgb-d Object Rmentioning

confidence: 99%

RGB-D-Based Object Recognition Using Multimodal Convolutional Neural Networks: A Survey

et al. 2019

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Object recognition in real-world environments is one of the fundamental and key tasks in computer vision and robotics communities. With the advanced sensing technologies and low-cost depth sensors, the high-quality RGB and depth images can be recorded synchronously, and the object recognition performance can be improved by jointly exploiting them. RGB-D-based object recognition has evolved from early methods that using hand-crafted representations to the current state-of-the-art deep learning-based methods. With the undeniable success of deep learning, especially convolutional neural networks (CNNs) in the visual domain, the natural progression of deep learning research points to problems involving larger and more complex multimodal data. In this paper, we provide a comprehensive survey of recent multimodal CNNs (MMCNNs)-based approaches that have demonstrated significant improvements over previous methods. We highlight two key issues, namely, training data deficiency and multimodal fusion. In addition, we summarize and discuss the publicly available RGB-D object recognition datasets and present a comparative performance evaluation of the proposed methods on these benchmark datasets. Finally, we identify promising avenues of research in this rapidly evolving field. This survey will not only enable researchers to get a good overview of the state-of-the-art methods for RGB-D-based object recognition but also provide a reference for other multimodal machine learning applications, e.g., multimodal medical image fusion, audiovisual speech recognition, and multimedia retrieval and generation.

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“…Some highest reliable unlabeled data will then be selected and added incrementally into the labeled training set along with their predicted labels. The procedure is repeated until convergence (Wu et al, 2018). The advantage of self-training is that it does not require a specific assumption, and as a result, it can be used in almost any situation.…”

Section: Semi-supervised Self-labelled Techniquesmentioning

confidence: 99%

Opinion Spam Detection based on Annotation Extension and Neural Networks

Liu¹,

Pang²

2019

CIS

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Online reviews play an increasingly important role in the purchase decisions of potential customers. Incidentally, driven by the desire to gain profit or publicity, spammers may be hired to write fake reviews and promote or demote the reputation of products or services. Correspondingly, opinion spam detection has attracted attention from both business and research communities in recent years. However, unlike other tasks such as news classification or blog classification, the existing review spam datasets are typically limited due to the expensiveness of human annotation, which may further affect detection performance even if excellent classifiers have been developed. We propose a novel approach in this paper to boost opinion spam detection performance by fully utilizing the existing labelled small-size dataset. We first design an annotation extension scheme that uses extra tree classifiers to train multiple estimators and then iteratively generate reliable labelled samples from unlabeled ones. Subsequently, we examine neural network scenarios on a newly extended dataset to learn the distributed representation. Experimental results suggest that the proposed approach has better generalization capability and improved performance than state-ofthe-art methods.

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Self-training semi-supervised classification based on density peaks of data

Cited by 100 publications

References 30 publications

Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning

Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning

RGB-D-Based Object Recognition Using Multimodal Convolutional Neural Networks: A Survey

Opinion Spam Detection based on Annotation Extension and Neural Networks

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