Top-<i>k</i> Feature Selection Framework Using Robust 0–1 Integer Programming

Zhang, Xiaoqin; Fan, Mingyu; Wang, Di; Zhou, Peng; Tao, Dacheng

doi:10.1109/tnnls.2020.3009209

Cited by 94 publications

(36 citation statements)

References 61 publications

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“…Moreover, with those properties we described, the AGOA can be applied to more scenarios, such as energy optimization problems, image segmentation problems, and the optimization of support vector machines [17], [27], [125]- [127], extreme learning machines [19], [20], [34], [128]- [130], and convolutional neural networks [131][132][133] that involve parameter optimization. Besides, the binary version of the AGOA can be used in feature selection problems [134]- [137] to cope with feature selection problems in the field of data mining.…”

Section: Discussionmentioning

confidence: 99%

Chaotic Arc Adaptive Grasshopper Optimization

et al. 2021

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The grasshopper optimization algorithm (GOA) has become one of the most widely used methods, regardless of its shortcomings in its performance and algorithm design. To further harmonize exploration and exploitation in the GOA, this paper proposed an enhanced variant of the primary method by incorporating chaos theory to address the problems of slow convergence, low solution accuracy, and stagnation. Structural code analysis and previous works reveal that the grasshopper algorithm's internal function is one of these shortcomings' primary sources. The proposed GOA-based method employed an adaptive arc function instead of the grasshopper algorithm's internal function to strengthen and balance the global exploration capabilities and local exploitation capacities without any expensive modifications. Additionally, the chaotic mapping strategy was adopted to update the individual positions of agents iteratively. The individuals near the current optimal solution were disturbed to regenerate the optimal solution and enhance the swarm's diversity. This idea improves the global inspection capacity and discourages falling into local optima. To verify the proposed technique's effectiveness, a comprehensive set of twenty-seven benchmark cases and three engineering design problems were used for validation. We compared the proposed GOA-based method with the WOA, SCA, GOA, DA, CSSA, CGSCA, CLPSO, GL25, and OBWOA. Additionally, we tested the designed GOA against LSHADE, SHADE, EBOwithCMAR, SaDE, MPEDE, and EPSDE. Simulation results demonstrated that the algorithm was substantially superior to the original technique. Its global optimization competence, search accuracy, and convergence performance were notably improved. The results expose the regulation of this internal factor, which significantly affects the quality of the results. Further information about this research and assistance with any request is available at http://aliasgharheidari.com. INDEX TERMSGrasshopper optimization algorithm, swarm intelligence, chaotic maps, engineering design problem.

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Section: Discussionmentioning

confidence: 99%

Chaotic Arc Adaptive Grasshopper Optimization

et al. 2021

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“…Network learning intelligent models have advantages over other methods such as flexibility over data, free assumptions in methodology, no requirement for professional statistical analysis before modeling, and can easily model complex multiparameter systems. is method can be very useful in finding complex nonlinear relationships when the database is high dimensional [47,[78][79][80][81]. Obtaining more efficient models to predict accidents will allow researchers to achieve a better performance in road safety.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…Modeling the severity of accidents in terms of their effective parameters makes it possible to predict the occurrence of accidents requiring relief equipment. Various feature selection and learning methods [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58] can also be applied as a preprocessing step to these prediction cases. In addition, using this model, the effect of each factor in intensifying accidents can be investigated.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis for Study of the Effect of Dark Clothing Color of Female Pedestrians on the Severity of Accident Using Machine Learning Methods

Hosseinian

Gilani

Mirbaha

et al. 2021

Mathematical Problems in Engineering

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The color and brightness of pedestrian clothing are among the factors that could increase the severity of their accidents due to the lack of visibility, especially at night. Today, as most Iranian females tend to wear hijab or dark clothing, the necessity of investigating female pedestrian accidents influenced by clothing color is important. Many studies have been performed to analyze the severity of pedestrian accidents, but a study has not yet been conducted to determine the effect of the dark clothing color of female pedestrians on the severity of accidents. Therefore, in this study, 12 independent variables affecting the severity of female pedestrian accidents such as clothing color, age, accident time, day, weather condition, education, pedestrian action, crossing facilities, crossing permit, job, road classification, and fault status were studied. Frequency analysis, Friedman test (FT), and Factor Analysis (FA) methods, as well as modeling methods of Multiple Logistic Regression (MLR) and Artificial Neural Networks (ANNs) using Multilayer Perceptron (MLP) and Radius Basis Function (RBF), were used. Results indicated that clothing color had a significant influence on pedestrian accidents, and chador and dark clothing color increased the probability of accidents, especially at night. The MLP model had a better prediction percentage than the rest, the prediction accuracy of which was 94.6%. Finally, safety solutions were presented according to the results to reduce pedestrian accidents and increase road safety.

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“…An artificial neural network is derived from the way of information process in human biological systems and consists of an interconnected group of elements called neurons [32][33][34][35]. Various architectures are used in neural networks [36][37][38][39][40][41]. Some of them include feedforward networks and recurrent networks.…”

Section: Neural Networkmentioning

confidence: 99%

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

Chafi

Afrakhte

2021

Mathematical Problems in Engineering

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Electrical load forecasting plays a key role in power system planning and operation procedures. So far, a variety of techniques have been employed for electrical load forecasting. Meanwhile, neural-network-based methods led to fewer prediction errors due to their ability to adapt properly to the consuming load's hidden characteristic. Therefore, these methods were widely accepted by the researchers. As the parameters of the neural network have a significant impact on its performance, in this paper, a short-term electrical load forecasting method using neural network and particle swarm optimization (PSO) algorithm is proposed, in which some neural network parameters including learning rate and number of hidden layers are determined in order to forecast electrical load using the PSO algorithm precisely. Then, the neural network with these optimized parameters is used to predict the short-term electrical load. In this method, a three-layer feedforward neural network trained by backpropagation algorithm is used beside an improved gbest PSO algorithm. Also, the neural network prediction error is defined as the PSO algorithm cost function. The proposed approach has been tested on the Iranian power grid using MATLAB software. The average of three indices beside graphical results has been considered to evaluate the performance of the proposed method. The simulation results reflect the capabilities of the proposed method in accurately predicting the electrical load.

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Top-k Feature Selection Framework Using Robust 0–1 Integer Programming

Cited by 94 publications

References 61 publications

Chaotic Arc Adaptive Grasshopper Optimization

Chaotic Arc Adaptive Grasshopper Optimization

Statistical Analysis for Study of the Effect of Dark Clothing Color of Female Pedestrians on the Severity of Accident Using Machine Learning Methods

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

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