Automatic optic disc detection using low-rank representation based semi-supervised extreme learning machine

Zhou, Wei; Qiao, Shaojie; Yi, Yugen; Han, Nan; Chen, Yuqi; Lei, Gang

doi:10.1007/s13042-019-00939-0

Cited by 20 publications

(10 citation statements)

References 53 publications

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“…erefore, it can be considered to increase the training features of the model, such as the emotional or sentiment features of news events or social media [69], so as to improve the prediction performance of the model from the perspective of feature selection. And then, we further apply our model on more application fields, such as gold price prediction, oil price prediction, foreign exchange price prediction, novelty detection [70], and optic disc detection [71]. In addition, a graph-based embedding technology will be introduced to solve the problem of time series prediction [72].…”

Section: Discussionmentioning

confidence: 99%

Stock Price Forecast Based on CNN-BiLSTM-ECA Model

Chen

Fang

Liang

et al. 2021

Scientific Programming

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Financial data as a kind of multimedia data contains rich information, which has been widely used for data analysis task. However, how to predict the stock price is still a hot research problem for investors and researchers in financial field. Forecasting stock prices becomes an extremely challenging task due to high noise, nonlinearity, and volatility of the stock price time series data. In order to provide better prediction results of stock price, a new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory (BiLSTM) network, and Attention Mechanism (AM). More specifically, CNN is utilized to extract the deep features of stock data for reducing the influence of high noise and nonlinearity. Then, BiLSTM network is employed to predict the stock price based on the extracted deep features. Meanwhile, a novel Efficient Channel Attention (ECA) module is introduced into the network model to further improve the sensitivity of the network to the important features and key information. Finally, extensive experiments are conducted on the three stock datasets such as Shanghai Composite Index, China Unicom, and CSI 300. Compared with the existing methods, the experimental results verify the effectiveness and feasibility of the proposed CNN-BILSTM-ECA network model, which can provide an important reference for investors to make decisions.

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Section: Discussionmentioning

confidence: 99%

Stock Price Forecast Based on CNN-BiLSTM-ECA Model

Chen

Fang

Liang

et al. 2021

Scientific Programming

Self Cite

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“…Usually, with the usage of the extracted features, a classification model can be trained which identifies the normal class versus abnormal class. Many classifiers have been employed to distinguish the two classes based on the extracted features, for instance, artificial neural network (ANN) [34], K-nearest neighbor (KNN) [32], support vector machine (SVM) [35], least square support vector machine 2 Wireless Communications and Mobile Computing (LS-SVM) [29], and extreme learning machine (ELM) [36].…”

Section: Machine Learning-(ml-) Basedmentioning

confidence: 99%

Unsupervised Anomaly Detection for Glaucoma Diagnosis

Zhou

Yuan²,

et al. 2021

Wireless Communications and Mobile Computing

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With the rapid development of high tech, Internet of Things (IoT) and artificial intelligence (AI) achieve a series of achievements in the healthcare industry. Among them, automatic glaucoma diagnosis is one of them. Glaucoma is second leading cause of blindness in the world. Although many automatic glaucoma diagnosis approaches have been proposed, they still face the following two challenges. First, the data acquisition of diseased images is extremely expensive, especially for disease with low occurrence, leading to the class imbalance. Second, large-scale labeled data are hard to obtain in medical image domain. The aforementioned challenges limit the practical application of these approaches in glaucoma diagnosis. To address these disadvantages, this paper proposes an unsupervised anomaly detection framework based on sparse principal component analysis (SPCA) for glaucoma diagnosis. In the proposed approach, we just employ the one-class normal (nonglaucoma) images for training, so the class imbalance problem can be avoided. Then, to distinguish the glaucoma (abnormal) images from the normal images, a feature set consisting of segmentation-based features and image-based features is extracted, which can capture the shape and textural changes. Next, SPCA is adopted to select the effective features from the feature set. Finally, with the usage of the extracted effective features, glaucoma diagnosis can be automatically accomplished via introducing the T 2 statistic and the control limit, overcoming the issue of insufficient labeled samples. Extensive experiments are carried out on the two public databases, and the experimental results verify the effectiveness of the proposed approach.

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“…To address the strong sensitivity of the classification performance of SSELM to the quality of popular graphs, She et al [35] proposed a regularized SSELM based on balanced graphs, combining label consistency graph (LCG) and sample similarity graph (SSG), and then optimizing the weight ratio of these two graphs to obtain an optimal neighborhood graph. To address the shortcoming that SSELM cannot mine information from nonlinear data, Zhou et al [36] proposed a semi-supervised extreme learning machine (LRR-SSELM) based on a low-rank representation, which introduces a nonlinear classifier and a low-rank representation (LRR), and the LRR can maintain the popular structure of the original data. To enhance the feature extraction and classification performance of SSELM, She et al [37] proposed a new hierarchical semi-supervised extremal learning machine (HSSELM) that uses the HELM method for automatic feature extraction of deep structures and then uses SSELM for classification tasks.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM

Yang

Xia

et al. 2021

Mathematics

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A novel semi-supervised learning method is proposed to better utilize labeled and unlabeled samples to improve classification performance. However, there is exists the limitation that Laplace regularization in a semi-supervised extreme learning machine (SSELM) tends to lead to poor generalization ability and it ignores the role of labeled information. To solve the above problems, a Joint Regularized Semi-Supervised Extreme Learning Machine (JRSSELM) is proposed, which uses Hessian regularization instead of Laplace regularization and adds supervised information regularization. In order to solve the problem of slow convergence speed and the easy to fall into local optimum of marine predator algorithm (MPA), a multi-strategy marine predator algorithm (MSMPA) is proposed, which first uses a chaotic opposition learning strategy to generate high-quality initial population, then uses adaptive inertia weights and adaptive step control factor to improve the exploration, utilization, and convergence speed, and then uses neighborhood dimensional learning strategy to maintain population diversity. The parameters in JRSSELM are then optimized using MSMPA. The MSMPA-JRSSELM is applied to logging oil formation identification. The experimental results show that MSMPA shows obvious superiority and strong competitiveness in terms of convergence accuracy and convergence speed. Also, the classification performance of MSMPA-JRSSELM is better than other classification methods, and the practical application is remarkable.

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Automatic optic disc detection using low-rank representation based semi-supervised extreme learning machine

Cited by 20 publications

References 53 publications

Stock Price Forecast Based on CNN-BiLSTM-ECA Model

Stock Price Forecast Based on CNN-BiLSTM-ECA Model

Unsupervised Anomaly Detection for Glaucoma Diagnosis

A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM

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