Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy

Geoderma ◽  
2020 ◽  
Vol 365 ◽  
pp. 114227 ◽  
Author(s):  
Leonardo Deiss ◽  
Andrew J. Margenot ◽  
Steve W. Culman ◽  
M. Scott Demyan
Keyword(s):  
Support Vector Machines ◽  
Soil Properties ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Support Vector ◽  
Mir Spectroscopy ◽  
Vector Machines

scholarly journals Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yudong Li ◽  
Zhongke Feng ◽  
Shilin Chen ◽  
Ziyu Zhao ◽  
Fengge Wang
Keyword(s):  
Neural Network ◽  
Support Vector Machines ◽  
Forest Fire ◽  
Bp Neural Network ◽  
Forest Fires ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Autonomous Region ◽  
Svm Model ◽  
Vector Machines

The study of forest fire prediction is of great environmental and scientific significance. China’s Guangxi Autonomous Region has a high incidence rate of forest fires. At present, there is little research on forest fires in this area. The application of the artificial neural network and support vector machines (SVM) in forest fire prediction in this area can provide data for forest fire prevention and control in Guangxi. In this paper, based on Guangxi’s 2010–2018 satellite monitoring hotspot data, meteorology, terrain, vegetation, infrastructure, and socioeconomic data, the researchers determined the main forest fire driving factors in Guangxi. They used feature selection and backpropagation neural networks and radial basis SVM to build forest fire prediction models. Finally, the researchers use the accuracy, precision, and area under the characteristic curve (ROC-AUC) and other indicators to evaluate the predictive performance of the two models. The results showed that the prediction accuracy of the BP neural network and SVM is 92.16% and 89.89%, respectively. As both results are over 85%, the requirements of prediction accuracy is met. These results can be used for forest fire prediction in the Guangxi Autonomous Region. Specifically, the accuracy of the BP neural network was 0.93, which was higher than that of the SVM model (0.89); the recall of the SVM model was 0.84, which was lower than the BANN model (0.92), and the AUC value of the SVM model was 0.95, which was lower than the BP neural network model. The obtained results confirm that the BP neural network model can provide more prediction accuracy than support vector machines and is therefore more suitable for forest fire prediction in Guangxi, China. This research provides the necessary theoretical basis and data support for application in the field of forestry of the Guangxi Autonomous Region, China.

Study on Prediction of Chaotic Time Series Using Least Squares Support Vector Machines

2014 ◽  
Vol 1061-1062 ◽  
pp. 935-938
Author(s):  
Xin You Wang ◽  
Guo Fei Gao ◽  
Zhan Qu ◽  
Hai Feng Pu
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Least Squares ◽  
Prediction Accuracy ◽  
Chaotic Time Series ◽  
Support Vector ◽  
Vector Machines ◽  
Online Prediction ◽  
Better Than

The predictions of chaotic time series by applying the least squares support vector machine (LS-SVM), with comparison with the traditional-SVM and-SVM, were specified. The results show that, compared with the traditional SVM, the prediction accuracy of LS-SVM is better than the traditional SVM and more suitable for time series online prediction.

scholarly journals Dual possibilistic regression models of support vector machines and application in power load forecasting

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772092163
Author(s):  
Xianfei Yang ◽  
Xiang Yu ◽  
Hui Lu
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Quadratic Programming ◽  
Regression Models ◽  
Load Forecasting ◽  
Interval Data ◽  
Support Vector ◽  
Power Load ◽  
Vector Machines ◽  
Power Load Forecasting

Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside and inside directions, respectively, which can estimate the inherent uncertainty existing in the given fuzzy phenomenon well. In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. And each quadratic programming problem containing fewer optimization variables makes the training speed of the proposed approach fast. Compared with other interval regression approaches based on support vector machines, such as quadratic loss support vector machine approach and two smaller quadratic programming problem support vector machine approach, the proposed approach is more efficient on several artificial datasets and power load dataset.

Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement

2018 ◽  
Vol 18 (3) ◽  
pp. 715-724 ◽  
Author(s):  
Xiao Li ◽  
Xin Liu ◽  
Clyde Zhengdao Li ◽  
Zhumin Hu ◽  
Geoffrey Qiping Shen ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Data Processing ◽  
Least Squares ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Displacement Monitoring ◽  
Processing Efficiency ◽  
Foundation Pit ◽  
Vector Machines

Foundation pit displacement is a critical safety risk for both building structure and people lives. The accurate displacement monitoring and prediction of a deep foundation pit are essential to prevent potential risks at early construction stage. To achieve accurate prediction, machine learning methods are extensively applied to fulfill this purpose. However, these approaches, such as support vector machines, have limitations in terms of data processing efficiency and prediction accuracy. As an emerging approach derived from support vector machines, least squares support vector machine improve the data processing efficiency through better use of equality constraints in the least squares loss functions. However, the accuracy of this approach highly relies on the large volume of influencing factors from the measurement of adjacent critical points, which is not normally available during the construction process. To address this issue, this study proposes an improved least squares support vector machine algorithm based on multi-point measuring techniques, namely, multi-point least squares support vector machine. To evaluate the effectiveness of the proposed multi-point least squares support vector machine approach, a real case study project was selected, and the results illustrated that the multi-point least squares support vector machine approach on average outperformed single-point least squares support vector machine in terms of prediction accuracy during the foundation pit monitoring and prediction process.

Regression Models Using Pattern Search Assisted Least Square Support Vector Machines

2005 ◽  
Vol 83 (8) ◽  
pp. 1030-1037 ◽  
Author(s):  
N.S. Patil ◽  
P.S. Shelokar ◽  
V.K. Jayaraman ◽  
B.D. Kulkarni
Keyword(s):  
Support Vector Machines ◽  
Regression Models ◽  
Pattern Search ◽  
Least Square ◽  
Support Vector ◽  
Vector Machines

scholarly journals Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2995 ◽  
Author(s):  
Marina Corral Bobadilla ◽  
Roberto Fernández Martínez ◽  
Rubén Lostado Lorza ◽  
Fátima Somovilla Gómez ◽  
Eliseo Vergara González
Keyword(s):  
Support Vector Machines ◽  
Vegetable Oils ◽  
Regression Models ◽  
Waste Cooking Oil ◽  
Biodiesel Production ◽  
Support Vector ◽  
Alkaline Catalyst ◽  
Heating Value ◽  
Cooking Oil ◽  
Vector Machines

The ever increasing fuel demands and the limitations of oil reserves have motivated research of renewable and sustainable energy resources to replace, even partially, fossil fuels, which are having a serious environmental impact on global warming and climate change, excessive greenhouse emissions and deforestation. For this reason, an alternative, renewable and biodegradable combustible like biodiesel is necessary. For this purpose, waste cooking oil is a potential replacement for vegetable oils in the production of biodiesel. Direct transesterification of vegetable oils was undertaken to synthesize the biodiesel. Several variables controlled the process. The alkaline catalyst that is used, typically sodium hydroxide (NaOH) or potassium hydroxide (KOH), increases the solubility and speeds up the reaction. Therefore, the methodology that this study suggests for improving the biodiesel production is based on computing techniques for prediction and optimization of these process dimensions. The method builds and selects a group of regression models that predict several properties of biodiesel samples (viscosity turbidity, density, high heating value and yield) based on various attributes of the transesterification process (dosage of catalyst, molar ratio, mixing speed, mixing time, temperature, humidity and impurities). In order to develop it, a Box-Behnken type of Design of Experiment (DoE) was designed that considered the variables that were previously mentioned. Then, using this DoE, biodiesel production features were decided by conducting lab experiments to complete a dataset with real production properties. Subsequently, using this dataset, a group of regression models—linear regression and support vector machines (using linear kernel, polynomial kernel and radial basic function kernel)—were constructed to predict the studied properties of biodiesel and to obtain a better understanding of the process. Finally, several biodiesel optimization scenarios were reached through the application of genetic algorithms to the regression models obtained with greater precision. In this way, it was possible to identify the best combinations of variables, both independent and dependent. These scenarios were based mainly on a desire to improve the biodiesel yield by obtaining a higher heating value, while decreasing the viscosity, density and turbidity. These conditions were achieved when the dosage of catalyst was approximately 1 wt %.

Soil type classification and estimation of soil properties using support vector machines

Geoderma ◽  
2010 ◽  
Vol 154 (3-4) ◽  
pp. 340-347 ◽  
Author(s):  
Miloš Kovačević ◽  
Branislav Bajat ◽  
Boško Gajić
Keyword(s):  
Support Vector Machines ◽  
Soil Properties ◽  
Soil Type ◽  
Support Vector ◽  
Vector Machines ◽  
Type Classification

Protein Folding Classification Through Multicategory Discrete SVM

2011 ◽  
pp. 116-130
Author(s):  
Carlotta Orsenigo ◽  
Carlo Vercellis
Keyword(s):  
Protein Folding ◽  
Support Vector Machines ◽  
Prediction Accuracy ◽  
Classification Problem ◽  
New Drugs ◽  
Support Vector ◽  
Multicategory Classification ◽  
Vector Machines ◽  
Benchmark Datasets ◽  
Folding Structure

In the context of biolife science, predicting the folding structure of a protein plays an important role for investigating its function and discovering new drugs. Protein folding recognition can be naturally cast in the form of a multicategory classification problem, that appears challenging due to the high number of folds classes. Thus, in the last decade several supervised learning methods have been applied in order to discriminate between proteins characterized by different folds. Recently, discrete support vector machines have been introduced as an effective alternative to traditional support vector machines. Discrete SVM have shown to outperform other competing classification techniques both on binary and multicategory benchmark datasets. In this paper, we adopt discrete SVM for protein folding classification. Computational tests performed on benchmark datasets empirically support the effectiveness of discrete SVM, which are able to achieve the highest prediction accuracy.

Boosted Decision Trees for Credit Scoring

2022 ◽  
pp. 270-292
Author(s):  
Luca Di Persio ◽  
Alberto Borelli
Keyword(s):  
Support Vector Machines ◽  
Decision Trees ◽  
Prediction Accuracy ◽  
Credit Scoring ◽  
Support Vector ◽  
Scoring Model ◽  
Vector Machines ◽  
Boosted Decision Trees ◽  
The One ◽  
Credit Scoring Model

The chapter developed a tree-based method for credit scoring. It is useful because it helps lenders decide whether to grant or reject credit to their applicants. In particular, it proposes a credit scoring model based on boosted decision trees which is a technique consisting of an ensemble of several decision trees to form a single classifier. The analysis used three different publicly available datasets, and then the prediction accuracy of boosted decision trees is compared with the one of support vector machines method.

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