A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection

Liu, Tong; Hu, Liang; Ma, Chao; Wang, Zhiyan; Chen, Huiling

doi:10.1080/00207721.2013.801096

Cited by 72 publications

(19 citation statements)

References 35 publications

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“…Previous studies [ 14 , 30 ] showed that the activation functions and hidden neurons have more or less impact on ELM performance. Therefore, these two factors were investigated in the following experiment.…”

Section: Resultsmentioning

confidence: 99%

Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach

et al. 2015

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The number of the overweight people continues to rise across the world. Studies have shown that being overweight can increase health risks, such as high blood pressure, diabetes mellitus, coronary heart disease, and certain forms of cancer. Therefore, identifying the overweight status in people is critical to prevent and decrease health risks. This study explores a new technique that uses blood and biochemical measurements to recognize the overweight condition. A new machine learning technique, an extreme learning machine, was developed to accurately detect the overweight status from a pool of 225 overweight and 251 healthy subjects. The group included 179 males and 297 females. The detection method was rigorously evaluated against the real-life dataset for accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic (ROC) curve) criterion. Additionally, the feature selection was investigated to identify correlating factors for the overweight status. The results demonstrate that there are significant differences in blood and biochemical indexes between healthy and overweight people (p-value < 0.01). According to the feature selection, the most important correlated indexes are creatinine, hemoglobin, hematokrit, uric Acid, red blood cells, high density lipoprotein, alanine transaminase, triglyceride, and γ-glutamyl transpeptidase. These are consistent with the results of Spearman test analysis. The proposed method holds promise as a new, accurate method for identifying the overweight status in subjects.

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Section: Resultsmentioning

confidence: 99%

Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach

et al. 2015

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“…After that, compared with the previous training approaches, ELM can significantly improve both learning speed and performance. Due to the effectiveness of ELM, it has been applied to solve a large number of practical problems such as medical analysis and diagnosis [27]- [32], face recognition [33].…”

Section: A Kernel Extreme Learning Machine (Kelm)mentioning

confidence: 99%

Evaluation of Sino Foreign Cooperative Education Project Using Orthogonal Sine Cosine Optimized Kernel Extreme Learning Machine

Zhu

Zhao

et al. 2020

IEEE Access

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This study aims to propose an efficient evaluation model for Sino foreign cooperative education projects, which can offer a reasonable reference for universities to deepen reform and innovation of education and further enhance the level of international education. The core engine of the model is the kernel extreme learning machine (KELM) model integrated with orthogonal learning (OL) strategy optimization. The introduction of the OL mechanism is to further strengthen the optimization capabilities of the basic SCA, which is devoted to promoting the KELM model to select the optimal parameter combination and feature subset and further enhance the KELM evaluation capability of Sino foreign cooperative education projects. To examine the performance of the proposed method, OLSCA is evaluated on 23 benchmark problems, comparison with eight other well-known methods. The experimental results have shown that the proposed OLSCA is prominently superior to existing methods on most functional problems. Meantime, OLSCA-KELM is compared against other machine learning approaches in dealing with the evaluation of education projects of Sino foreign cooperation. The simulation results illustrate that the presented OLSCA-KELM obtains better performance of classification and higher stability on all four indicators. Therefore, it is evident that the presented OLSCA-KELM can be an effective solution for the evaluation of Sino foreign cooperative education projects. INDEX TERMS Sine cosine algorithm, swarm intelligence, sino foreign cooperative education project, Kernel extreme learning machine, parameter optimization. HUILING CHEN received the Ph.D. degree from the Department of Computer Science and Technology, Jilin University, China. He is currently an Associate Professor with the College of Computer Science and Artificial Intelligence, Wenzhou University, China. He has published more than 100 articles in international journals and conference proceedings, including Information Sciences, Pattern Recognition, Future Generation Computer System, Expert Systems with Applications, Knowledge-Based Systems, Neurocomputing, PAKDD, and among others. His current research interests include machine learning and data mining, as well as their applications to medical diagnosis and bankruptcy prediction. He is also a Reviewer of many journals such as Applied Soft Computing, Artificial Intelligence in Medicine, Knowledge-Based Systems, and Future Generation Computer System. He has been serving as an Associate Editor for IEEE ACCESS.

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“…ELM can significantly advance learning rapidity and performance compared to previous training methods. Due to its excellent performance, it has gained a wide range of applications [86][87][88][89].…”

Section: C22 Lbolboa-kelm Model a Description Of Kelmmentioning

confidence: 99%

Prediction Optimization of Cervical Hyperextension Injury: Kernel Extreme Learning Machines With Orthogonal Learning Butterfly Optimizer and Broyden- Fletcher-Goldfarb-Shanno Algorithms

et al. 2020

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In this research, X-ray and MRI images of patients suffering from cervical hyperextension injury are investigated. Also, radiographic images are collected from patients who suffered from trauma but without cervical hyperextension injury. The core engine algorithm of the optimized prediction model is kernel extreme learning machine (KELM), and the input data is 17 factors that may cause cervical hyperextension injury. As the optimization core, we utilized the Butterfly optimization algorithm (BOA). Up to now, few improved variants of BOA have been reported. The original BOA converges slowly and quickly falls into a locally optimal solution. An enhanced BOA based on orthogonal learning, Levy flight, and an exploitation engine is proposed in this paper to relieve these two shortcomings, which is called LBOLBOA. Orthogonal learning is utilized to construct guidance vectors for guiding agents toward the global optimum solution aiming to increase the accuracy of the solutions. Also, Levy flight and Broyden-Fletcher-Goldfarb-Shanno mechanisms are utilized to enrich the intensification propensities of BOA and stagnation avoidance. The proposed LBOLBOA is used to deal with continuous function optimization and machine learning problems, including parameter optimization of KELM. We rigorously verified this variant using a comprehensive set of the benchmark test suite and real-world dataset on cervical hyperextension injury. The results indicate that LBOLBOA can achieve improved performance in dealing with the function optimization and machine learning problems, especially the capability for prediction of cervical hyperextension injury.

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A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection

Cited by 72 publications

References 35 publications

Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach

Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach

Evaluation of Sino Foreign Cooperative Education Project Using Orthogonal Sine Cosine Optimized Kernel Extreme Learning Machine

Prediction Optimization of Cervical Hyperextension Injury: Kernel Extreme Learning Machines With Orthogonal Learning Butterfly Optimizer and Broyden- Fletcher-Goldfarb-Shanno Algorithms

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