Safe Semi-Supervised Extreme Learning Machine for EEG Signal Classification

She, Qingshan; Hu, Bo; Gan, Haiyong; Yi, Fan; Nguyen, Thinh; Potter, Thomas; Zhang, Yingchun

doi:10.1109/access.2018.2868713

Cited by 29 publications

(11 citation statements)

References 30 publications

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“…al. [22,23] improve the ELM algorithm and propose semi-supervised ELM and safe semi-supervised ELM for EEG signals classification respectively. The results show that classification accuracy has significant improvement over ELM.…”

Section: Methodsmentioning

confidence: 99%

“…Jia et al [21] propose a new semi-supervised deep learning algorithm combined with the restricted Boltzmann machine and apply it for EEG signals classification. She et al [22,23] improve the ELM algorithm and propose semi-supervised ELM and safe semi-supervised ELM for EEG signals classification respectively. The results show that classification accuracy has significant improvement over ELM.…”

Section: Introductionmentioning

confidence: 99%

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Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning

et al. 2019

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In recent years, the accurate and real-time classification of electroencephalogram (EEG) signals has drawn increasing attention in the application of brain-computer interface technology (BCI). Supervised methods used to classify EEG signals have gotten satisfactory results. However, unlabeled samples are more frequent than labeled samples, so how to simultaneously utilize limited labeled samples and many unlabeled samples becomes a research hotspot. In this paper, we propose a new graph-based semi-supervised broad learning system (GSS-BLS), which combines the graph label propagation method to obtain pseudo-labels and then trains the GSS-BLS classifier together with other labeled samples. Three BCI competition datasets are used to assess the GSS-BLS approach and five comparison algorithms: BLS, ELM, HELM, LapSVM and SMIR. The experimental results show that GSS-BLS achieves satisfying Cohen’s kappa values in three datasets. GSS-BLS achieves the better results of each subject in the 2-class and 4-class datasets and has significant improvements compared with original BLS except subject C6. Therefore, the proposed GSS-BLS is an effective semi-supervised algorithm for classifying EEG signals.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning

et al. 2019

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“…In this section, to evaluate the performance of our method, we compared it with the most advanced classification algorithms, including ELM [23], SS-ELM [30], Safe-SSELM [31].…”

Section: Methodsmentioning

confidence: 99%

“…In particular, the inclusion of unlabeled samples in some cases may reduce the performance of the SS-ELM. In response to the above problem, SHE et al [31] proposed a safe semi-supervised extreme learning machine (Safe-SSELM). Experimental results show that the performance of Safe-SSELM is rarely significantly lower than that of ELM using only labeled samples.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Safe Semi-Supervised Extreme Machine Learning

Yuan

2019

IEEE Access

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Semi-supervised learning (SSL) based on manifold regularization (MR) is an excellent learning framework. However, the performance of SSL heavily depends on the construction of manifold graph and the safety degrees of unlabeled samples. Due to the construction of manifold graph and safety degrees of unlabeled samples are usually pre-construct before classification and fixed during the classification learning process, which results independent with the subsequent classification. Aiming at the above problems, we propose a unified adaptive safe semi-supervised learning (Adap-SaSSL) framework. This framework adaptively constructs a manifold graph while adaptively calculating the safety degrees of unlabeled samples. Specifically, the weights of manifold graph and its parameters, as well as the safety degrees of unlabeled samples will be optimized during the learning process rather than being calculated in advance. Finally, we then develop and implement a adaptive safe classification method based on the Adap-SaSSL framework, which is called adaptive safe semi-supervised extreme learning machine (AdSafe-SSELM). Experimental results on artificial, benchmark and image datasets show that the performance of AdSafe-SSELM is effective and reliable compared to other algorithms. INDEX TERMS Semi-supervised learning (SSL), extreme learning machine, adaptive safety degree, adaptive graph, manifold regularization (MR).

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“…Semi-supervised learning [31] provides a way to take advantage of unlabeled samples. Semisupervised learning has a wide range of applications in object detection [32], biomedical signal processing [33] and image processing.…”

Section: Semi-supervised Learningmentioning

confidence: 99%

A Recursive Ensemble Learning Approach With Noisy Labels or Unlabeled Data

2019

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For many tasks, the successful application of deep learning relies on having large amounts of training data, labeled to a high standard. But much of the data in real-world applications suffer from label noise. Data annotation is much more expensive and resource-consuming than data collection, somewhat restricting the successful deployment of deep learning to applications where there are very large and welllabeled datasets. To address this problem, we propose a recursive ensemble learning approach in order to maximize the utilization of data. A disagreement-based annotation method and different voting strategies are the core ideas of the proposed method. Meanwhile, we provide guidelines for how to choose the most suitable among many candidate neural networks, with a pruning strategy that provides convenience. The approach is effective especially when the original dataset contains a significant label noise. We conducted experiments on the datasets of Cats versus Dogs, in which significant amounts of label noise were present, and on the CIFAR-10 dataset, achieving promising results.

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Safe Semi-Supervised Extreme Learning Machine for EEG Signal Classification

Cited by 29 publications

References 30 publications

Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning

Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning

Adaptive Safe Semi-Supervised Extreme Machine Learning

A Recursive Ensemble Learning Approach With Noisy Labels or Unlabeled Data

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