Regression random machines: An ensemble support vector regression model with free kernel choice

Ara, Anderson; Maia, Mateus; Louzada, Francisco; Macêdo, Samuel

doi:10.1016/j.eswa.2022.117107

Cited by 13 publications

(3 citation statements)

References 49 publications

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“…First, SVR possesses good properties that differ from the other models. By using the kernel trick that can transfer the training data from the input space to a high-dimensional-, even infinite-dimensional-feature, space via implicitly defined nonlinear mapping, SVR can solve nonlinear problems in arbitrarily high-dimensional-feature spaces that are indexed by multidimensional data structures [77]. We included some classification predictors, such as subjective social status, gender, religion, and race, in the current study.…”

Section: Discussionmentioning

confidence: 99%

Predictive Models of Life Satisfaction in Older People: A Machine Learning Approach

Shen

Yin

Chen

2023

IJERPH

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Studies of life satisfaction in older adults have been conducted extensively through empirical research, questionnaires, and theoretical analysis, with the majority of these studies basing their analyses on simple linear relationships between variables. However, most real-life relationships are complex and cannot be approximated with simple correlations. Here, we first investigate predictors correlated with life satisfaction in older adults. Then, machine learning is used to generate several predictive models based on a large sample of older adults (age ≥ 50 years; n = 34,630) from the RAND Health and Retirement Study. Results show that subjective social status, positive emotions, and negative emotions are the most critical predictors of life satisfaction. The Support Vector Regression (SVR) model exhibited the highest prediction accuracy for life satisfaction in older individuals among several models, including Multiple Linear Regression (MLR), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator Regression (LASSO), K Nearest Neighbors (KNN), and Decision Tree Regression (DT) models. Although the KNN and DT models exhibited better model fitting than MLR, RR, and LASSO, their performances were poor in terms of model validation and model generalization. These results indicate that machine learning is superior to simple correlations for understanding life satisfaction among older adults.

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Section: Discussionmentioning

confidence: 99%

Predictive Models of Life Satisfaction in Older People: A Machine Learning Approach

Shen

Yin

Chen

2023

IJERPH

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“…An SVM is a kind of machine learning method developed by Vapnik three decades ago [93]. It can analyse data for classification and regression analysis.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Building Energy Prediction Models and Related Uncertainties: A Review

Chang

Dong

2022

Buildings

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Building energy usage has been an important issue in recent decades, and energy prediction models are important tools for analysing this problem. This study provides a comprehensive review of building energy prediction models and uncertainties in the models. First, this paper introduces three types of prediction methods: white-box models, black-box models, and grey-box models. The principles, strengths, shortcomings, and applications of every model are discussed systematically. Second, this paper analyses prediction model uncertainties in terms of human, building, and weather factors. Finally, the research gaps in predicting building energy consumption are summarised in order to guide the optimisation of building energy prediction methods.

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“…The error function most commonly used in traditional support vector machine algorithms is the least-squared sum error function [22]. In support vector machines used to solve regression analysis problems, with the introduction of an insensitive error function, we commonly use the minimax regularisation error function.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Research on Accurate Estimation Method of Eucalyptus Biomass Based on Airborne LiDAR Data and Aerial Images

Wang

Shi³

et al. 2022

Sustainability

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Forest biomass is a key index to comprehend the changes of ecosystem productivity and forest growth and development. Accurate acquisition of single tree scale biomass information is of great significance to the protection, management and monitoring of forest resources. LiDAR technology can penetrate the forest canopy and obtain information on the vertical structure of the forest. Aerial photography technology has the advantages of low cost and high speed, and can obtain information on the horizontal structure of the forest. Therefore, in this study, multispectral imagery and LiDAR data were integrated, and a part of the Zengcheng Forest Farm in Guangdong Province was selected as the study area. Large-scale and high-precision Eucalyptus biomass estimation research was gradually carried out by screening influencing factors and establishing models. This study compared and analysed the performance of multiple stepwise regression methods, random forest algorithms, support vector machine algorithms and decision tree algorithms for Eucalyptus biomass estimation to determine the best method for Eucalyptus biomass estimation. The results demonstrated that the accuracy of the model established by the machine learning method was higher than that of the linear regression model, and in the machine learning model, the random forest model had the best performance on both the training set (R2 = 0.9346, RMSE = 8.8399) and the test set (R2 = 0.8670, RMSE = 15.0377). RF was more suitable for the biomass estimation of Eucalyptus in this study. The spatial resolution of Eucalyptus biomass distribution was 0.05 m in this study, which had higher accuracy and was more accurate. It can provide data reference for the details about biomass distribution of Eucalyptus in the majority of provinces, and has certain practical reference significance.

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Regression random machines: An ensemble support vector regression model with free kernel choice

Cited by 13 publications

References 49 publications

Predictive Models of Life Satisfaction in Older People: A Machine Learning Approach

Predictive Models of Life Satisfaction in Older People: A Machine Learning Approach

Building Energy Prediction Models and Related Uncertainties: A Review

Research on Accurate Estimation Method of Eucalyptus Biomass Based on Airborne LiDAR Data and Aerial Images

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