A novel F-SVM based on FOA for improving SVM performance

Gu, Qinghua; Chang, Yinxin; Li, Xinhong; Chang, Zhaozhao; Feng, Zhidong

doi:10.1016/j.eswa.2020.113713

Cited by 33 publications

(13 citation statements)

References 26 publications

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“…Consequently, they make a huge contribution to real-world problems such as cloud computing and task scheduling [ 18 , 19 ], wireless sensor network localization and routing [ 20 , 21 ], numerous fields of medicine [ 22 , 23 , 24 ], prediction of COVID-19 cases [ 25 ], anomaly detection [ 26 ], etc. [ 27 , 28 ].…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

Basha

Bačanin

Vukobrat

et al. 2021

Sensors

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The research presented in this manuscript proposes a novel Harris Hawks optimization algorithm with practical application for evolving convolutional neural network architecture to classify various grades of brain tumor using magnetic resonance imaging. The proposed improved Harris Hawks optimization method, which belongs to the group of swarm intelligence metaheuristics, further improves the exploration and exploitation abilities of the basic algorithm by incorporating a chaotic population initialization and local search, along with a replacement strategy based on the quasi-reflection-based learning procedure. The proposed method was first evaluated on 10 recent CEC2019 benchmarks and the achieved results are compared with the ones generated by the basic algorithm, as well as with results of other state-of-the-art approaches that were tested under the same experimental conditions. In subsequent empirical research, the proposed method was adapted and applied for a practical challenge of convolutional neural network design. The evolved network structures were validated against two datasets that contain images of a healthy brain and brain with tumors. The first dataset comprises well-known IXI and cancer imagining archive images, while the second dataset consists of axial T1-weighted brain tumor images, as proposed in one recently published study in the Q1 journal. After performing data augmentation, the first dataset encompasses 8.000 healthy and 8.000 brain tumor images with grades I, II, III, and IV and the second dataset includes 4.908 images with Glioma, Meningioma, and Pituitary, with 1.636 images belonging to each tumor class. The swarm intelligence-driven convolutional neural network approach was evaluated and compared to other, similar methods and achieved a superior performance. The obtained accuracy was over 95% in all conducted experiments. Based on the established results, it is reasonable to conclude that the proposed approach could be used to develop networks that can assist doctors in diagnostics and help in the early detection of brain tumors.

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Section: Preliminaries and Related Workmentioning

confidence: 99%

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

Basha

Bačanin

Vukobrat

et al. 2021

Sensors

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“…SVM is a linear classifier with the largest interval in the feature space. Its learning strategy is to maximize the interval, and it is ultimately transformed into a convex quadratic programming problem to solve [44]. Based on the existing training set,…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

A Novel Carbon Price Fluctuation Trend Prediction Method Based on Complex Network and Classification Algorithm

Wang

2021

Complexity

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Carbon price fluctuation is affected by both internal market mechanisms and the heterogeneous environment. Moreover, it is a complex dynamic evolution process. This paper focuses on carbon price fluctuation trend prediction. In order to promote the accuracy of the forecasting model, this paper proposes the idea of integrating network topology information into carbon price data; that is, carbon price data are mapped into a complex network through a visibility graph algorithm, and the network topology information is extracted. The extracted network topology structure information is used to reconstruct the data, which are used to train the model parameters, thus improving the prediction accuracy of the model. Five prediction models are selected as the benchmark model, and the price data of the EU and seven pilot carbon markets in China from June 19, 2014, to October 9, 2020, are chosen as the sample for empirical analysis. The research finds that the integration of network topology information can significantly improve the price trend prediction of the five benchmark models for the EU carbon market. However, there are great differences in the accuracy improvement effects of China’s seven pilot carbon market price forecasts. Moreover, the forecasting accuracy of the four carbon markets (i.e., Guangdong, Chongqing, Tianjin, and Shenzhen) has improved slightly, but the prediction accuracy of the carbon price trend in Beijing, Shanghai, and Hubei has not improved. We analyze the reasons leading to this result and offer suggestions to improve China’s pilot carbon market.

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“…SVM can solve the nonlinear problem well. It can map the training samples from the original space to a higher dimensional space, so that the samples are linearly separable in this space (Gu et al, 2021). Therefore, the nonlinear SVM prediction function model can be expressed as shown in equation ( 5).…”

Section: Svmmentioning

confidence: 99%

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

Lian

2021

Wind Engineering

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The accuracy of wind power prediction directly affects the operation cost of power grid and is the result of power grid supply and demand balance. Therefore, how to improve the prediction accuracy of wind power is very important. In order to improve the prediction accuracy of wind power, a prediction model based on wavelet denoising and improved slime mold algorithm optimized support vector machine is proposed. The wavelet denoising algorithm is used to denoise the wind power data, and then the support vector machine is used as the prediction model. Because the prediction results of support vector machine are greatly affected by model parameters, an improved slime mold optimization algorithm with random inertia weight mechanism is used to determine the best penalty factor and kernel function parameters in support vector machine model. The effectiveness of the proposed prediction model is verified by using two groups actually collected wind power data. Seven prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, the performance indicators, the Pearson’s correlation coefficient, the DM test, box-plot distribution, the results show that the proposed prediction model has high prediction accuracy.

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A novel F-SVM based on FOA for improving SVM performance

Cited by 33 publications

References 26 publications

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

A Novel Carbon Price Fluctuation Trend Prediction Method Based on Complex Network and Classification Algorithm

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

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