scholarly journals An Efficient Feature Weighting Method for Support Vector Regression

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Minghua Xie ◽  
Lili Xie ◽  
Peidong Zhu
Keyword(s):  
Support Vector Regression ◽  
Pso Algorithm ◽  
Feature Weighting ◽  
Support Vector ◽  
Weighting Method ◽  
Feature Weights ◽  
Optimal Weights ◽  
Proposed Model ◽  
Synthetic Datasets ◽  
Maximal Information Coefficient

Support vector regression (SVR) is a powerful kernel-based method which has been successfully applied in regression problems. Regarding the feature-weighted SVR algorithms, its contribution to model output has been taken into account. However, the performance of the model is subject to the feature weights and the time consumption on training. In the paper, an efficient feature-weighted SVR is proposed. Firstly, the value constraint of each weight is obtained according to the maximal information coefficient which reveals the relationship between each input feature and output. Then, the constrained particle swarm optimization (PSO) algorithm is employed to optimize the feature weights and the hyperparameters simultaneously. Finally, the optimal weights are used to modify the kernel function. Simulation experiments were conducted on four synthetic datasets and seven real datasets by using the proposed model, classical SVR, and some state-of-the-art feature-weighted SVR models. The results show that the proposed method has the superior generalization ability within acceptable time.

scholarly journals Spatiotemporal Traffic Flow Prediction with KNN and LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Xianglong Luo ◽  
Danyang Li ◽  
Yu Yang ◽  
Shengrui Zhang
Keyword(s):  
Support Vector Regression ◽  
Traffic Flow ◽  
Prediction Models ◽  
Short Term Memory ◽  
Transportation Systems ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Proposed Model

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

An integrated fuzzy support vector regression and the particle swarm optimization algorithm to predict indoor thermal comfort

2016 ◽  
Vol 25 (8) ◽  
pp. 1248-1258 ◽  
Author(s):  
Fayçal Megri ◽  
Ahmed Cherif Megri ◽  
Riadh Djabri
Keyword(s):  
Particle Swarm Optimization ◽  
Thermal Comfort ◽  
Support Vector Regression ◽  
Thermal Sensation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Support Vector ◽  
Statistical Regression ◽  
Swarm Optimization ◽  
Comfort Indices

The thermal comfort indices are usually identified using empirical thermal models based on the human balanced equations and experimentations. In our paper, we propose a statistical regression method to predict these indices. To achieve this goal, first, the fuzzy support vector regression (FSVR) identification approach was integrated with the particle swarm optimization (PSO) algorithm. Then PSO was used as a global optimizer to optimize and select the hyper-parameters needed for the FSVR model. The radial basis function (RBF) kernel was used within the FSVR model. Afterward, these optimal hyper-parameters were used to forecast the thermal comfort indices: predicted mean vote (PMV), predicted percentage dissatisfied (PPD), new standard effective temperature (SET*), thermal discomfort (DISC), thermal sensation (TSENS) and predicted percent dissatisfied due to draft (PD). The application of the proposed approach on different data sets gave successful prediction and promising results. Moreover, the comparisons between the traditional Fanger model and the new model further demonstrate that the proposed model achieves even better identification performance than the original FSVR technique.

scholarly journals Fuzzified Pipes Dataset to Predict Failure Rates by Hybrid SVR-PSO Algorithm

2016 ◽  
Vol 10 (7) ◽  
pp. 29 ◽  
Author(s):  
Jaber Soltani ◽  
Moosa Kalanaki ◽  
Mohammad Soltani
Keyword(s):  
Support Vector Regression ◽  
Pso Algorithm ◽  
Experimental Results ◽  
Support Vector ◽  
Failure Rates ◽  
Input Output ◽  
Generalization Capability ◽  
Comparative Tests

This paper proposes a Support Vector Regression (SVR) based on Fuzzified Input-output Variables which has good comprehensibility as well as satisfactory generalization capability. SVM provides a mechanism to predict data from training ones. Then, results from proposed Fuzzified SVR-PSO (FSVR-PSO) model are compared with other methods; comparative tests are performed using pipe failures data. The analysis and the experimental results show this method has high comprehensibility as well as satisfactory generalization capability.

Framework for Environment Quality Monitoring Using Radial Support Vector Regression

2020 ◽  
Vol 17 (5) ◽  
pp. 2176-2180
Author(s):  
K. Uday Kiran ◽  
Shaikh Shakeela ◽  
B. Sakthi Kumar ◽  
K. Rajesh Kumar ◽  
N. Shravan Kumar ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Fundamental Solutions ◽  
Performance Measure ◽  
World Health ◽  
Support Vector ◽  
Air Contamination ◽  
Environment Quality ◽  
Data Prediction ◽  
Proposed Model ◽  
Health Organization

Air contamination is the difficult issue that one must consider and is brought about by injurious gases present in the climate. For example, Carbon Dioxide, Carbon Monoxide, Sulfur Dioxide and so on. The dimensions of quality of the air shifts starting with one spot then onto the next. As indicated by WHO (World Health Organization) air contamination is the fifth significant reason for heart related diseases, hypertension, poor sustenance and tobacco smoking. Observing the measure of destructive gases over a specific zone can lessen the odds of jeopardize to individuals and caution to take prudent steps and do fundamental solutions for direct the emanation of harmful climatic gases. The present paper manages the observing of the polluted gases utilizing gas sensor which is connected with Node MCU. The levels of polluted gases sent through web to cloud stages utilizing MQTT conventions. The information is then sent to ThingSpeak cloud which can be additionally dissected from anyplace in world. Further analysis of the gas levels and estimations of future status of the air quality must be done and in order to carry out the same, high end strategies are adopted like Machine learning (ML) computation called Support Vector Regression (SVR) using Radial Basis Function (RBF), which is good in class regression analysis in the field of data prediction and significantly utilized for information anticipating. The implementation of proposed ML algorithm enhances the chances of forecasting close gas levels to actual values. And mean square error analysis gives the performance measure of the proposed model.

Short-Term Forecasting of Natural Gas Consumption Using Factor Selection Algorithm and Optimized Support Vector Regression

2018 ◽  
Vol 141 (3) ◽  
Author(s):  
Nan Wei ◽  
Changjun Li ◽  
Chan Li ◽  
Hanyu Xie ◽  
Zhongwei Du ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Natural Gas ◽  
Support Vector Regression ◽  
Series Data ◽  
Support Vector ◽  
Selection Algorithm ◽  
Short Term ◽  
Proposed Model ◽  
Gas Consumption ◽  
Natural Gas Consumption

Forecasting of natural gas consumption has been essential for natural gas companies, customers, and governments. However, accurate forecasting of natural gas consumption is difficult, due to the cyclical change of the consumption and the complexity of the factors that influence the consumption. In this work, we constructed a hybrid artificial intelligence (AI) model to predict the short-term natural gas consumption and examine the effects of the factors in the consumption cycle. The proposed model combines factor selection algorithm (FSA), life genetic algorithm (LGA), and support vector regression (SVR), namely, as FSA-LGA-SVR. FSA is used to select factors automatically for different period based on correlation analysis. The LGA optimized SVR is utilized to provide the prediction of time series data. To avoid being trapped in local minima, the hyper-parameters of SVR are determined by LGA, which is enhanced due to newly added “learning” and “death” operations in conventional genetic algorithm. Additionally, in order to examine the effects of the factors in different period, we utilized the recent data of three big cities in Greece and divided the data into 12 subseries. The prediction results demonstrated that the proposed model can give a better performance of short-term natural gas consumption forecasting compared to the estimation value of existing models. Particularly, the mean absolute range normalized errors of the proposed model in Athens, Thessaloniki, and Larisa are 1.90%, 2.26%, and 2.12%, respectively.

scholarly journals A Clustering-Based Hybrid Support Vector Regression Model to Predict Container Volume at Seaport Sanitary Facilities

2020 ◽  
Vol 10 (23) ◽  
pp. 8326
Author(s):  
Juan Jesús Ruiz-Aguilar ◽  
José Antonio Moscoso-López ◽  
Daniel Urda ◽  
Javier González-Enrique ◽  
Ignacio Turias
Keyword(s):  
Support Vector Regression ◽  
Linear Models ◽  
Hybrid Approach ◽  
Accurate Prediction ◽  
Support Vector ◽  
Self Organizing Map ◽  
Support Vector Regression Model ◽  
Proposed Model ◽  
Key Factor ◽  
Predicted Values

An accurate prediction of freight volume at the sanitary facilities of seaports is a key factor to improve planning operations and resource allocation. This study proposes a hybrid approach to forecast container volume at the sanitary facilities of a seaport. The methodology consists of a three-step procedure, combining the strengths of linear and non-linear models and the capability of a clustering technique. First, a self-organizing map (SOM) is used to decompose the time series into smaller clusters easier to predict. Second, a seasonal autoregressive integrated moving averages (SARIMA) model is applied in each cluster in order to obtain predicted values and residuals of each cluster. These values are finally used as inputs of a support vector regression (SVR) model together with the historical data of the cluster. The final prediction result integrates the prediction results of each cluster. The experimental results showed that the proposed model provided accurate prediction results and outperforms the rest of the models tested. The proposed model can be used as an automatic decision-making tool by seaport management due to its capacity to plan resources in advance, avoiding congestion and time delays.

scholarly journals Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

2018 ◽  
Vol 10 (10) ◽  
pp. 3434 ◽  
Author(s):  
Omer Azeez ◽  
Biswajeet Pradhan ◽  
Helmi Shafri
Keyword(s):  
Support Vector Regression ◽  
Urban Areas ◽  
Road Traffic ◽  
Spatial Prediction ◽  
Absolute Error ◽  
Support Vector ◽  
Vehicular Emissions ◽  
Support Vector Regression Model ◽  
Promising Tool ◽  
Proposed Model

Transportation infrastructures play a significant role in the economy as they provide accessibility services to people. Infrastructures such as highways, road networks, and toll plazas are rapidly growing based on changes in transportation modes, which consequently create congestions near toll plaza areas and intersections. These congestions exert negative impacts on human health and the environment because vehicular emissions are considered as the main source of air pollution in urban areas and can cause respiratory and cardiovascular diseases and cancer. In this study, we developed a hybrid model based on the integration of three models, correlation-based feature selection (CFS), support vector regression (SVR), and GIS, to predict vehicular emissions at specific times and locations on roads at microscale levels in an urban areas of Kuala Lumpur, Malaysia. The proposed model comprises three simulation steps: first, the selection of the best predictors based on CFS; second, the prediction of vehicular carbon monoxide (CO) emissions using SVR; and third, the spatial simulation based on maps by using GIS. The proposed model was developed with seven road traffic CO predictors selected via CFS (sum of vehicles, sum of heavy vehicles, heavy vehicle ratio, sum of motorbikes, temperature, wind speed, and elevation). Spatial prediction was conducted based on GIS modelling. The vehicular CO emissions were measured continuously at 15 min intervals (recording 15 min averages) during weekends and weekdays twice per day (daytime, evening-time). The model’s results achieved a validation accuracy of 80.6%, correlation coefficient of 0.9734, mean absolute error of 1.3172 ppm and root mean square error of 2.156 ppm. In addition, the most appropriate parameters of the prediction model were selected based on the CFS model. Overall, the proposed model is a promising tool for traffic CO assessment on roads.

scholarly journals The effects of splitters on the downstream scour hole of overflow spillways: application of support vector regression

2021 ◽  
Author(s):  
Mohammad Ehsan Asadi ◽  
Seyed Taghi Omid Naeeni ◽  
Reza Kerachian
Keyword(s):  
Support Vector Regression ◽  
Experimental Models ◽  
Data Driven ◽  
Support Vector ◽  
Method Performance ◽  
Statistical Measures ◽  
Proposed Model ◽  
Scour Hole ◽  
Scour Hole Dimensions ◽  
Hole Dimensions

Abstract One of the most effective ways to reduce the water jet erosion power during dam overflow is to use splitters on the lower side of spillway. The dimensions of scouring holes and their location in the dam basin should be accurately determined. Experimental models and data driven techniques can be effectively used for estimating the dimensions of scouring holes. The focus of this paper is evaluating the effects of splitters on the downstream scour hole of overflow spillways and providing an optimized splitter configuration. The Support Vector Regression (SVR) method performance in predicting the scour hole dimensions and its location downstream of the dam has been examined using 116 experimental data. In order to evaluate the efficiency of the proposed model, we used different statistical measures. The results show that the presence of splitters decreases the slope of downstream scouring in all situations. It is also shown that the SVR method can accurately estimate the dimensions of the scour hole and its location.

scholarly journals Forecasting Time Series Movement Direction with Hybrid Methodology

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Salwa Waeto ◽  
Khanchit Chuarkham ◽  
Arthit Intarasit
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Hybrid Model ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Movement Direction ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Proposed Model ◽  
Hybrid Methodology

Forecasting the tendencies of time series is a challenging task which gives better understanding. The purpose of this paper is to present the hybrid model of support vector regression associated with Autoregressive Integrated Moving Average which is formulated by hybrid methodology. The proposed model is more convenient for practical usage. The tendencies modeling of time series for Thailand’s south insurgency is of interest in this research article. The empirical results using the time series of monthly number of deaths, injuries, and incidents for Thailand’s south insurgency indicate that the proposed hybrid model is an effective way to construct an estimated hybrid model which is better than the classical time series model or support vector regression. The best forecast accuracy is performed by using mean square error.

scholarly journals A Forecasting Approach Combining Self-Organizing Map with Support Vector Regression for Reservoir Inflow during Typhoon Periods

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Gwo-Fong Lin ◽  
Tsung-Chun Wang ◽  
Lu-Hsien Chen
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Self Organizing Map ◽  
Support Vector Regression Model ◽  
Proposed Model ◽  
Forecasting Performance ◽  
Short Lead Time ◽  
Reservoir Inflow ◽  
Reservoir Watershed ◽  
Self Organizing

This study describes the development of a reservoir inflow forecasting model for typhoon events to improve short lead-time flood forecasting performance. To strengthen the forecasting ability of the original support vector machines (SVMs) model, the self-organizing map (SOM) is adopted to group inputs into different clusters in advance of the proposed SOM-SVM model. Two different input methods are proposed for the SVM-based forecasting method, namely, SOM-SVM1 and SOM-SVM2. The methods are applied to an actual reservoir watershed to determine the 1 to 3 h ahead inflow forecasts. For 1, 2, and 3 h ahead forecasts, improvements in mean coefficient of efficiency (MCE) due to the clusters obtained from SOM-SVM1 are 21.5%, 18.5%, and 23.0%, respectively. Furthermore, improvement in MCE for SOM-SVM2 is 20.9%, 21.2%, and 35.4%, respectively. Another SOM-SVM2 model increases the SOM-SVM1 model for 1, 2, and 3 h ahead forecasts obtained improvement increases of 0.33%, 2.25%, and 10.08%, respectively. These results show that the performance of the proposed model can provide improved forecasts of hourly inflow, especially in the proposed SOM-SVM2 model. In conclusion, the proposed model, which considers limit and higher related inputs instead of all inputs, can generate better forecasts in different clusters than are generated from the SOM process. The SOM-SVM2 model is recommended as an alternative to the original SVR (Support Vector Regression) model because of its accuracy and robustness.

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