Drought prediction in the Yunnan–Guizhou Plateau of China by coupling the estimation of distribution algorithm and the extreme learning machine

Li, Qiongfang; Du, Yao; Liu, Zhennan; Zhou, Zhengmo; Lu, Guoping; Chen, Qihui

doi:10.1007/s11069-022-05361-4

Cited by 6 publications

(4 citation statements)

References 89 publications

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“…Weights and hidden biases of a feedforward neural network [133] PBIL Weights and structure of a multilayer perceptron [134] PBIL Weights and structure of a multilayer perceptron [135] UMDA G c…”

Section: Edas In Supervised Learningmentioning

confidence: 99%

“…The EDAs used for the search of the optimal weights were PBIL [129], [130], UMDA G c [131], [132] and MIMIC G c [131]. The input weights and hidden biases are the variables to be optimized in [133] in single layer feedforward neural networks, coupling an EDA (PBIL) and the extreme learning machine model.…”

Section: Edas In Supervised Learningmentioning

confidence: 99%

“…First, EDAs for association rule mining in traffic flow prediction and in blood index analysis are found in [115] and [114], respectively. Second, in supervised learning, EDAs contribute to finding: the support vector regression parameters applied to software reliability prediction [159]; the parameters of a full connected multilayer perceptron with a hidden layer in time series forecasting problems [137]; the weights in convolutional neural networks for text categorization [140]; the input weights and hidden biases in single layer feedforward neural networks for drought prediction in China [133]; the regularized parameters of logistic regression models in microarray data classification [165]; and in different feature selection problems: of clinical findings in cirrhotic patients treated with transjugular intrahepatic portosystemic shunt [198], of peakbins in mass spectrometry data [199], of nucleotides for splice site prediction in Arabidopsis thaliana [200], of traffic and connection features in intrusion detection [166], and to predict the likely method (negotiation, won at trial, etc.) of settlement for a claim in a legal business [201].…”

Section: Real-world Applications Of Edas In Machine Learningmentioning

confidence: 99%

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Estimation of Distribution Algorithms in Machine Learning: A Survey

Larrañaga,

Bielza

2024

IEEE Trans. Evol. Computat.

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The automatic induction of machine learning models capable of addressing supervised learning, feature selection, clustering and reinforcement learning problems requires sophisticated intelligent search procedures. These searches are usually performed in the possible model structure spaces, leading to combinatorial optimization problems, and in the parameter spaces, where it is necessary to solve continuous optimization problems. This paper reviews how the estimation of distribution algorithms, a kind of evolutionary algorithm, can be used to address these problems. Topics include preprocessing, mining association rules, selecting variables, searching for the optimal supervised learning model (both probabilistic and nonprobabilistic models), finding the best hierarchical, partitional or probabilistic clustering, obtaining the optimal policy in reinforcement learning and performing inference and structural learning in Bayesian networks for association discovery. Interesting guidelines for future work in this area are also provided.

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Section: Edas In Supervised Learningmentioning

confidence: 99%

Section: Edas In Supervised Learningmentioning

confidence: 99%

Section: Real-world Applications Of Edas In Machine Learningmentioning

confidence: 99%

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Estimation of Distribution Algorithms in Machine Learning: A Survey

Larrañaga,

Bielza

2024

IEEE Trans. Evol. Computat.

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“…This approach enhances both the predictive accuracy and the generalizability of the models across various domains. The integrated models, such as the particle swarm optimization-extreme learning machine (PSO-ELM) [21], bat optimization algorithm-extreme learning machine (BOA-ELM) [22], the estimation distribution algorithm-extreme learning machine (EDA-ELM) [23], genetic algorithm-support vector machine (GA-SVM) [24], and whale optimization algorithm-random forest (WOA-RF) [25], incorporate intelligent optimization algorithms into machine learning models with the aim of optimizing parameter selection and enhancing model performance. These methods have demonstrated efficacy in various fields, including financial market forecasting, bioinformatics, environmental monitoring, and energy consumption prediction.…”

Section: Introductionmentioning

confidence: 99%

Soil Salinity Inversion in Yellow River Delta by Regularized Extreme Learning Machine Based on ICOA

Wang,

Zhang

et al. 2024

Remote Sensing

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Soil salinization has seriously affected agricultural production and ecological balance in the Yellow River Delta region. Rapid and accurate monitoring of soil salinity has become an urgent need. Traditional machine learning models tend to fall into local optimal values during the learning process, which reduces their accuracy. This paper introduces Circle map to enhance the crayfish optimization algorithm (COA), which is then integrated with the regularized extreme learning machine (RELM) model, aiming to improve the accuracy of soil salinity content (SSC) inversion in the Yellow River Delta region. We employed Landsat5 TM remote sensing images and measured salinity data to develop spectral indices, such as the band index, salinity index, vegetation index, and comprehensive index, selecting the optimal modeling variable group through Pearson correlation analysis and variable projection importance analysis. The back propagation neural network (BPNN), RELM, and improved crayfish optimization algorithm–regularized extreme learning machine (ICOA-RELM) models were constructed using measured data and selected variable groups for SSC inversion. The results indicate that the ICOA-RELM model enhances the R2 value by an average of about 0.1 compared to other models, particularly those using groups of variables filtered by variable projection importance analysis as input variables, which showed the best inversion effect (test set R2 value of 0.75, MAE of 0.198, RMSE of 0.249). The SSC inversion results indicate a higher salinization degree in the coastal regions of the Yellow River Delta and a lower degree in the inland areas, with moderate saline soil and severe saline soil comprising 48.69% of the total area. These results are consistent with the actual sampling results, which verify the practicability of the model. This paper’s methods and findings introduce an innovative and practical tool for monitoring and managing salinized soils in the Yellow River Delta, offering significant theoretical and practical benefits.

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