Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients

Zhao, Xuehua; Zhang, Xiang; Cai, Zhennao; Tian, Xun; Wang, Xianqin; Huang, Ying; Chen, Huiling; Hu, Lufeng

doi:10.1016/j.compbiolchem.2018.11.017

Cited by 297 publications

(121 citation statements)

References 34 publications

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“…Therefore, a multiobjective idea of these techniques has been adapted to solve the problem of feature selection and shows a great success. GWO has recently gained much consideration for tackling the problem of feature selection in many fields such as benchmarks problems as in [12], [33], [34] , facial, voice, speech and handwriting recognition as in [35], [36], [37], EMG signal classification [38], disease diagnosis [39], [11], [40], [41], gene selection and intrusion detection systems. [42], [43].…”

Section: Related Workmentioning

confidence: 99%

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

et al. 2020

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Feature selection or dimensionally reduction can be considered as a multi-objective minimization problem with two objectives: minimizing the number of features and minimizing the error rate simultaneously. Despite being a multiobjective problem, most existing approaches treat feature selection as a single-objective optimization problem. Recently, Multiobjective Grey Wolf optimizer (MOGWO) was proposed to solve multi-objective optimization problem. However, MOGWO was originally designed for continuous optimization problems and hence, it cannot be utilized directly to solve multi-objective feature selection problems which are inherently discrete in nature. Therefore, in this research, a binary version of MOGWO based on sigmoid transfer function called BMOGW-S is developed to optimize feature selection problems. A wrapper based Artificial Neural Network (ANN) is used to assess the classification performance of a subset of selected features. To validate the performance of the proposed method, 15 standard benchmark datasets from the UCI repository are employed. The proposed BMOGWO-S was compared with MOGWO with a tanh transfer function and Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO). The results showed that the proposed BMOGWO-S can effectively determine a set of non-dominated solutions. The proposed method outperforms the existing multi-objective approaches in most cases in terms of features reduction as well as classification error rate while benefiting from a lower computational cost.

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Section: Related Workmentioning

confidence: 99%

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

et al. 2020

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“…Also, the total ranking obtained for models is featured in Table 4. The same process is provided in other machine learning-based studies (e.g., [30][31][32][33], [36][37][38][39], [41], [42], [75], [76]). A graphical exhibition of the results is also presented, based on a colour intensity model.…”

Section: Model Assessment In the Percentage Of Drag Reduction Predictionmentioning

confidence: 99%

“…Most recently developed prediction techniques employed the computer science advances and intended to find a reliable solution in solving engineering and medical related problems. In this sense, the technique of extreme machine learning approaches [30][31][32]33], Harris hawks optimization [34,35], spatial adjacent histogram [36], fruit fly optimization [37], chaotic moth-flame optimization [38,39,40], multi-swarm whale optimizer [41], grey wolf optimization [42] can be mentioned. Such multi-disciplinary techniques are widely used as analysis tools in most complex engineering projects such as building information modelling [43,44], sustainable sediment management in hydropower [45], contractors' dynamic price competition in mega projects [46], emotion recognition and image sharing [47], wireless sensor networks [48,49], big data application [50,51], landslide prediction over a large region [52], Digital Neuromorphic Architecture [53].…”

Section: Introductionmentioning

confidence: 99%

Soft computing method for predicting pressure drop reduction in crude oil pipelines based on machine learning methods

Moayedi

Foong

Nguyen

2020

J Braz. Soc. Mech. Sci. Eng.

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In the oil industry, the drag-reducing agent has been used to reduce turbulent friction of fluids. The main effort of this study is to examine the feasibility of four novel machine learning models, namely multilayer perceptron, M5Rules, decision table (DT), and trees M5P to estimate the percentage of drag reduction. Then, the mentioned methods are utilized to identify a relationship between the input and output parameters of the crude oil pipeline system. The parameter percentage of drag reduction was taken as the essential output. In contrast, the input parameters selected the flow rate of oil, polymer concentration, kind of polymer, temperature, as well as pipe diameter and roughness. The predicted results obtained by the tools mentioned above were evaluated according to several known statistical indices, namely coefficient of determination (R 2), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) as well as novel ranking systems of color intensity rating and total ranking method. The training and testing results of the DT learning method for the

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“…Since its introduction, SCA has successfully found its applications for many practical problems [13][14][15][16][17][18]. However, like other intelligent algorithms [19][20][21][22][23][24][25], the original SCA is easy to fall into the local optimum when solving the practical problems. In this study, we try to use the chaotic local search (CLS) strategy to enhance the local search capability of SCA, termed as CSCA.…”

Section: Introductionmentioning

confidence: 99%

Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

Lin

Chen

et al. 2019

Mathematical Problems in Engineering

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Under the background of "innovation and entrepreneurship," how to scientifically and rationally choose employment or independent entrepreneurship according to their own comprehensive situation is of great significance to the planning and development of their own career and the social adaptation of university personnel training. This study aims to develop an adaptive support vector machine framework, called RF-CSCA-SVM, for predicting college students' entrepreneurial intention in advance; that is, students choose to start a business or find a job after graduation. RF-CSCA-SVM combines random forest (RF), support vector machine (SVM), sine cosine algorithm (SCA), and chaotic local search. In this framework, RF is used to select the most important factors; SVM is employed to establish the relationship model between the factors and the students' decision to choose to start their own business or look for jobs. SCA is used to tune the optimal parameters for SVM. Additionally, chaotic local search is utilized to enhance the search capability of SCA. A total of 300 students were collected to develop the predictive model. To validate the developed method, other four meta-heuristic based SVM methods were used for comparison in terms of classification accuracy, Matthews Correlation Coefficients (MCC), sensitivity, and specificity. The experimental results demonstrate that the proposed method can be regarded as a promising success with the excellent predictive performance. Promisingly, the established adaptive SVM framework might serve as a new candidate of powerful tools for entrepreneurial intention prediction.

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Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients

Cited by 297 publications

References 34 publications

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

Soft computing method for predicting pressure drop reduction in crude oil pipelines based on machine learning methods

Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

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