scholarly journals The Application of Improved Random Forest Algorithm on the Prediction of Electric Vehicle Charging Load

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3207 ◽  
Author(s):  
Yiqi Lu ◽  
Yongpan Li ◽  
Da Xie ◽  
Enwei Wei ◽  
Xianlu Bao ◽  
...  
Keyword(s):  
Random Forest ◽  
Electric Vehicle ◽  
Regression Tree ◽  
Current Data ◽  
Classification And Regression Tree ◽  
Prediction Algorithm ◽  
Random Forest Algorithm ◽  
Application Form ◽  
Charging Station ◽  
Cart Algorithm

To cope with the increasing charging demand of electric vehicle (EV), this paper presents a forecasting method of EV charging load based on random forest algorithm (RF) and the load data of a single charging station. This method is completed by the classification and regression tree (CART) algorithm to realize short-term forecast for the station. At the same time, the prediction algorithm of the daily charging capacity of charging stations with different scales and locations is proposed. By combining the regression and classification algorithms, the effective learning of a large amount of historical charging data is completed. The characteristic data is divided from different aspects, realizing the establishment of RF and the effective prediction of fluctuate charging load. By analyzing the data of each charging station in Shenzhen from the aspect of time and space, the algorithm is put into practice. The application form of current data in the algorithm is determined, and the accuracy of the prediction algorithm is verified to be reliable and practical. It can provide a reference for both power suppliers and users through the prediction of charging load.

Using machine learning techniques to develop prediction models for detecting unpaid credit card customers

2020 ◽  
Vol 39 (5) ◽  
pp. 6073-6087
Author(s):  
Meltem Yontar ◽  
Özge Hüsniye Namli ◽  
Seda Yanik
Keyword(s):  
Decision Tree ◽  
Credit Card ◽  
Banking Sector ◽  
Performance Metrics ◽  
Prediction Models ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Cart Algorithm

Customer behavior prediction is gaining more importance in the banking sector like in any other sector recently. This study aims to propose a model to predict whether credit card users will pay their debts or not. Using the proposed model, potential unpaid risks can be predicted and necessary actions can be taken in time. For the prediction of customers’ payment status of next months, we use Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification and Regression Tree (CART) and C4.5, which are widely used artificial intelligence and decision tree algorithms. Our dataset includes 10713 customer’s records obtained from a well-known bank in Taiwan. These records consist of customer information such as the amount of credit, gender, education level, marital status, age, past payment records, invoice amount and amount of credit card payments. We apply cross validation and hold-out methods to divide our dataset into two parts as training and test sets. Then we evaluate the algorithms with the proposed performance metrics. We also optimize the parameters of the algorithms to improve the performance of prediction. The results show that the model built with the CART algorithm, one of the decision tree algorithm, provides high accuracy (about 86%) to predict the customers’ payment status for next month. When the algorithm parameters are optimized, classification accuracy and performance are increased.

scholarly journals Statistical Analysis of Treatment Planning Parameters for Prediction of Delivery Quality Assurance Failure for Helical Tomotherapy

2020 ◽  
Vol 19 ◽  
pp. 153303382097969
Author(s):  
Kyung Hwan Chang ◽  
Young Hyun Lee ◽  
Byung Hun Park ◽  
Min Cheol Han ◽  
Jihun Kim ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Treatment Planning ◽  
Planning Process ◽  
Helical Tomotherapy ◽  
Regression Tree ◽  
Roc Curves ◽  
Classification And Regression Tree ◽  
Variable Model ◽  
Predicted Probability ◽  
Cart Algorithm

Purpose: This study aimed to investigate the parameters with a significant impact on delivery quality assurance (DQA) failure and analyze the planning parameters as possible predictors of DQA failure for helical tomotherapy. Methods: In total, 212 patients who passed or failed DQA measurements were retrospectively included in this study. Brain (n = 43), head and neck (n = 37), spinal (n = 12), prostate (n = 36), rectal (n = 36), pelvis (n = 13), cranial spinal irradiation and a treatment field including lymph nodes (n = 24), and other types of cancer (n = 11) were selected. The correlation between DQA results and treatment planning parameters were analyzed using logistic regression analysis. Receiver operating characteristic (ROC) curves, areas under the curves (AUCs), and the Classification and Regression Tree (CART) algorithm were used to analyze treatment planning parameters as possible predictors for DQA failure. Results: The AUC for leaf open time (LOT) was 0.70, and its cut-off point was approximately 30%. The ROC curve for the predicted probability calculated when the multivariate variable model was applied showed an AUC of 0.815. We confirmed that total monitor units, total dose, and LOT were significant predictors for DQA failure using the CART. Conclusions: The probability of DQA failure was higher when the percentage of LOT below 100 ms was higher than 30%. The percentage of LOT below 100 ms should be considered in the treatment planning process. The findings from this study may assist in the prediction of DQA failure in the future.

Remote Sense Image Classification Based on CART Algorithm

2013 ◽  
Vol 864-867 ◽  
pp. 2782-2786
Author(s):  
Bao Hua Yang ◽  
Shuang Li
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Regression Tree ◽  
Remote Sensing Image ◽  
Classification And Regression Tree ◽  
Classification Method ◽  
Remote Sense ◽  
Data Source ◽  
Cart Algorithm ◽  
Tree Method

This papers deals with the study of the algorithm of classification method based on decision tree for remote sensing image. The experimental area is located in the Xiangyang district, the data source for the 2010 satellite images of SPOT and TM fusion. Moreover, classification method based on decision tree is optimized with the help of the module of RuleGen and applied in regional remote sensing image of interest. The precision of Maximum likelihood ratio is 95.15 percent, and 94.82 percent for CRAT. Experimental results show that the classification method based on classification and regression tree method is as well as the traditional one.

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