Babysitting hyperparameter optimization and 10-fold-cross-validation to enhance the performance of ML methods in predicting wind speed and energy generation

Malakouti, Seyed matin

doi:10.1016/j.iswa.2023.200248

Cited by 11 publications

(6 citation statements)

References 34 publications

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“…It provided each data the opportunity to become test data. K in this context referred to the number of multiples used for the division between training data and test data, this study uses K=10 [29], [30].…”

Section: Discussionmentioning

confidence: 99%

Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques

Lestari,

Yulmaini,

Aswin

et al. 2024

IJECE

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Recommendation systems are often implemented in e-commerce and micro, small, and medium enterprises (MSMEs) marketplaces to improve consumer services by providing product recommendations according to their interests. However, it still faces problems, namely sparsity and cold start, thus affecting the quality of recommendations. This research proposes clustering and imputation techniques to overcome this problem. The clustering technique used is k-means, while the missing value imputation method uses average values. The imputation results are then implemented in the k-nearest neighbor (KNN) and naïve Bayes algorithms and evaluated based on performance accuracy. Experimental results show an increase in accuracy of 16.48% in the KNN algorithm from 83.52% to 100%. Meanwhile, the naïve Bayes algorithm increased accuracy by 35.30% from 64.70% to 100%.

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Section: Discussionmentioning

confidence: 99%

Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques

Lestari,

Yulmaini,

Aswin

et al. 2024

IJECE

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“…In other words, all data is utilized to train the ML models after 10 iterations -lowering the bias. What's more, every iteration's model weights for the convolutional layers are continuously updated, which also adds the effectiveness of training (Malakouti, 2023).…”

Section: Semantic Segmentationmentioning

confidence: 99%

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

Shi,

Xiang,

Ying

et al. 2024

Preprint

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Measuring and predicting Carbon Emission (CE) is important to enabling the main culprit of various urgent environmental issues including global warming. However, prior studies did not fully incorporate the impact of micro-level urban streetscapes, which might lead to biased prediction of urban CE. To fill the gap, we developed an effective framework to predict residential CE in urban areas from widely existing and publicly available street-view images (SVI) using machine learning. First, we used a semantic segmentation algorithm to classify more than 30 streetscape elements from SVI images to describe the built environment whose features might affect residential and transportation CE. Second, based on the streetscapes quantified, we trained a 10-fold cross-validation method with various machine learning models to predict the CE at the 1KM grid level using CE data from the PlanetData. We found first, built environment features such as sidewalks, roads, fences, buildings, and walls are significantly correlated with the residential CE. Second, the presence of buildings and subtle streetscape features (e.g., walls, fences) indicates higher-density residential areas which are related to more residential CE. Third, vegetation (e.g., trees and grass) are reversely related to residential CE. Our findings shed light on the feasibility of using a single and open data source (i.e., the SVI) to effectively model neighborhood-level CE for regions across diverse urban forms. Our framework is useful for urban planners to inform new town development and urban regeneration towards the low CE goals.

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“…The hyperparameter settings were a reasonable selection, tuning, and combination based on considering the prediction accuracy and generalization of the above machine learning models. To enhance the predictive ability of machine learning algorithms, a 10-fold cross-validation method was used to evaluate the models [45]. Flow chart of the model training and evaluation process is shown in Figure 3, and the characteristics and specific implementation settings of each algorithm are introduced as follows.…”

Section: Modelingmentioning

confidence: 99%

The Prediction of Flow Stress in the Hot Compression of a Ni-Cr-Mo Steel Using Machine Learning Algorithms

Pan,

Song,

Gao

et al. 2024

Processes

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The constitutive model refers to the mapping relationship between the stress and deformation conditions (such as strain, strain rate, and temperature) after being loaded. In this work, the hot deformation behavior of a Ni-Cr-Mo steel was investigated by conducting isothermal compression tests using a Gleeble-3800 thermal simulator with deformation temperatures ranging from 800 °C to 1200 °C, strain rates ranging from 0.01 s−1 to 10 s−1, and deformations of 55%. To analyze the constitutive relation of the Ni-Cr-Mo steel at high temperatures, five machine learning algorithms were employed to predict the flow stress, namely, back-propagation artificial neural network (BP-ANN), Random Committee, Bagging, k-nearest neighbor (k-NN), and a library for support vector machines (libSVM). A comparative study between the experimental and the predicted results was performed. The results show that correlation coefficient (R), root mean square error (RMSE), mean absolute value error (MAE), mean square error (MSE), and average absolute relative error (AARE) obtained from the Random Committee on the testing set are 0.98897, 8.00808 MPa, 5.54244 MPa, 64.12927 MPa2 and 5.67135%, respectively, whereas the metrics obtained via other algorithms are all inferior to the Random Committee. It suggests that the Random Committee can predict the flow stress of the steel more effectively.

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Babysitting hyperparameter optimization and 10-fold-cross-validation to enhance the performance of ML methods in predicting wind speed and energy generation

Cited by 11 publications

References 34 publications

Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques

Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

The Prediction of Flow Stress in the Hot Compression of a Ni-Cr-Mo Steel Using Machine Learning Algorithms

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