Predicting Ozone Layer Concentration Using Multivariate Adaptive Regression Splines, Random Forest and Classification and Regression Tree

2017 ◽  
pp. 140-152 ◽  
Author(s):  
Sanjiban Sekhar Roy ◽  
Chitransh Pratyush ◽  
Cornel Barna
Keyword(s):  
Random Forest ◽  
Regression Tree ◽  
Ozone Layer ◽  
Multivariate Adaptive Regression Splines ◽  
Classification And Regression Tree ◽  
Regression Splines ◽  
Adaptive Regression ◽  
Classification And Regression ◽  
Adaptive Regression Splines

Classification of 3G Mobile Phone Customers

2009 ◽  
pp. 2862-2870
Author(s):  
Ankur Jain ◽  
Lalit Wangikar ◽  
Martin Ahrens ◽  
Ranjan Rao ◽  
Suddha Sattwa Kundu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Tree ◽  
Multivariate Adaptive Regression Splines ◽  
Classification And Regression Tree ◽  
The Third ◽  
Adaptive Regression ◽  
Classification And Regression ◽  
3G Mobile Phone ◽  
Adaptive Regression Splines

In this article we discuss how we have predicted the third generation (3G) customers using logistic regression analysis and statistical tools like Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), and other variables derived from the raw variables. The basic idea reflected in this paper is that the performance of logistic regression using raw variables standalone can be improved upon, by the use for various functions of the raw variables and dummies representing potential segments of the population

Classification Of 3G Mobile Phone Customers

2011 ◽  
pp. 2247-2254
Author(s):  
Ankur Jain ◽  
Lalit Wangikar ◽  
Martin Ahrens ◽  
Ranjan Rao ◽  
Suddha Sattwa Kundu ◽  
...  
Keyword(s):  
Regression Tree ◽  
Multivariate Adaptive Regression Splines ◽  
Classification And Regression Tree ◽  
Third Generation ◽  
The Third ◽  
Adaptive Regression ◽  
Classification And Regression ◽  
3G Mobile Phone ◽  
Adaptive Regression Splines

In this article we discuss how we have predicted the third generation (3G) customers using lo-gistic regression analysis and statistical tools like Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), and other variables derived from the raw variables. The basic idea reflected in this paper is that the performance of logistic regression using raw variables standalone can be improved upon, by the use for various functions of the raw variables and dummies representing potential segments of the population.

Classification Of 3G Mobile Phone Customers

2008 ◽  
pp. 2558-2565
Author(s):  
Ankur Jain ◽  
Lalit Wangikar ◽  
Martin Ahrens ◽  
Ranjan Rao ◽  
Suddha Sattwa Kundu ◽  
...  
Keyword(s):  
Regression Tree ◽  
Multivariate Adaptive Regression Splines ◽  
Classification And Regression Tree ◽  
Third Generation ◽  
The Third ◽  
Adaptive Regression ◽  
Classification And Regression ◽  
3G Mobile Phone ◽  
Adaptive Regression Splines

In this article we discuss how we have predicted the third generation (3G) customers using lo-gistic regression analysis and statistical tools like Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), and other variables derived from the raw variables. The basic idea reflected in this paper is that the performance of logistic regression using raw variables standalone can be improved upon, by the use for various functions of the raw variables and dummies representing potential segments of the population.

scholarly journals Novel Approaches to Modelling Flammability Characteristics of Polymethyl Methacrylate (PMMA) via Multivariate Adaptive Regression Splines and Random Forest Methods

2020 ◽  
pp. 1-14
Author(s):  
Mohammed Okoe Alhassan ◽  
Michael Boakye Osei
Keyword(s):  
Random Forest ◽  
Polymethyl Methacrylate ◽  
Polymeric Materials ◽  
Multivariate Adaptive Regression Splines ◽  
Coefficient Of Determination ◽  
Regression Splines ◽  
Soft Computing Techniques ◽  
Flammability Characteristics ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

Soft-computing techniques for fire safety parameter predictions in flammability studies are essential for describing a material fire behaviour. This study proposed, two novel Artificial Intelligence developed models, Multivariate Adaptive Regression Splines (MARS) and Random Forest (RF) methods, to model and predict peak heat release rate (pHRR) of Polymethyl methacrylate (PMMA) from Microscale Combustion Calorimetry (MCC) experiment. From the statistical analysis, MARS presented the highest coefficient of determination (R2) values of (0.9998) and (0.9996) for training and testing respectively, with low MAD, MAPE and RMSE values. Comparatively, MARS outperformed RF in the predictions of pHRR, through its model algorithms that generated optimized equations for pHRR predictions, covering all non-linearity points of the experimental data. Amongst the input variables (sample mass, THR, HRC, pTemp and pTime), heating rate (β), highly influenced pHRR outcome predictions from MARS and RF models. However, to validate the performance and applicability of the proposed models. Results of MARS and RF were benchmarked with that from Artificial Neural Network (ANN) methods. The MARS and RF models observed the least error deviation when compared with pHRR results for PMMA from the ANN models. This study therefore, recommends the adoption of MARS and RF in the predictions of flammability characteristics of polymeric materials.

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