scholarly journals Tree-Structured Regression Model Using a Projection Pursuit Approach

2021 ◽  
Vol 11 (21) ◽  
pp. 9885
Author(s):  
Hyunsun Cho ◽  
Eun-Kyung Lee
Keyword(s):  
Random Forest ◽  
Regression Model ◽  
Classification Tree ◽  
Projection Pursuit ◽  
Regression Tree ◽  
Projection Pursuit Regression ◽  
Single Tree ◽  
Variable Space ◽  
Independent Variable ◽  
Structured Regression

In this paper, we propose a new tree-structured regression modelthe projection pursuit regression tree.a new tree-structured regression model—the projection pursuit regression tree—is proposed. It combines the projection pursuit classification tree with the projection pursuit regression. The main advantage of the projection pursuit regression tree is exploring the independent variable space in each range of the dependent variable. Additionally, it retains the main properties of the projection pursuit classification tree. The projection pursuit regression tree provides several methods of assigning values to the final node, which enhances predictability. It shows better performance than CART in most cases and sometimes beats random forest with a single tree. This development makes it possible to find a better explainable model with reasonable predictability.

Study on Reliability of Hydraulic Brake System Based on Projection Pursuit Regression

2012 ◽  
Vol 198-199 ◽  
pp. 966-969
Author(s):  
Yu Cai Dong ◽  
Ge Hua Fan ◽  
Liang Hai Yi ◽  
Ling Zhang
Keyword(s):  
Regression Model ◽  
Projection Pursuit ◽  
Training Sample ◽  
Scientific Basis ◽  
Brake System ◽  
Projection Pursuit Regression ◽  
Hydraulic Brake ◽  
Regression Theory

The projection pursuit regression theory is applied to analysis the reliability of vehicle hydraulic brake system and build the projection pursuit regression model. This model on training sample fitting effect is good and shows extremely strong adaptability. We predict the reliability of certain type hydraulic brake system by this model which provides scientific basis for research on reliability of hydraulic brake system.

scholarly journals ANALISIS RANDOM FOREST PADA KLASIFIKASI CART KETIDAKTEPATAN WAKTU KELULUSAN MAHASISWA UNIVERSITAS TERBUKA

2019 ◽  
Vol 13 (3) ◽  
pp. 177-184
Author(s):  
Gede Suwardika Suwardika ◽  
I Ketut Putu Suniantara
Keyword(s):  
Random Forest ◽  
Classification Tree ◽  
Regression Tree ◽  
University Student ◽  
Classification And Regression Tree ◽  
Random Forest Method ◽  
Student Graduation ◽  
Stability And Accuracy ◽  
Learning Data

Classification and Regression Tree (CART) is one of the classification methods that are popularly used in various fields. The method is considered capable of dealing with various data conditions. However, the CART method has weaknesses in the classification tree prediction, which is less stable in changes in learning data which will cause major changes in the results of the classification tree prediction. Improving the predictions of the CART classification tree, an ensemble random forest method was developed that combines many classification trees to improve stability and determine classification predictions. This study aims to improve CART predictive stability and accuracy with Random Forest. The case used in this study is the classification of inaccuracies in Open University student graduation. The results of the analysis show that random forest is able to increase the accuracy of the classification of the inaccuracy of student graduation that reaches convergence with the prediction of classification reaching 93.23%.

Application of Projection Pursuit Regression in Equipment Indemnificatory Valuation and Forecast

2013 ◽  
Vol 411-414 ◽  
pp. 2111-2114
Author(s):  
Lian Jun Zhu ◽  
Hong Yan Li ◽  
Yu Cai Dong ◽  
Tian Yuan Jiang ◽  
Ge Hua Fan
Keyword(s):  
Regression Model ◽  
Relative Error ◽  
Projection Pursuit ◽  
Influential Factors ◽  
Average Relative Error ◽  
Projection Pursuit Regression ◽  
Good Balance ◽  
Training Samples ◽  
Good Forecast

The Theory of Projection Pursuit Regression is applied in the equipment indemnificatory valuation and forecast to establish the projection pursuit regression model. After fitting the training samples, this model strikes a good balance between the valuation value and its relevant influential factors, demonstrating a good fitting effect with the average relative error of only 2.1522% . After predicting the test samples, it shows a good forecast effect with the relative error of only-0.4069%, thus providing basis for equipment indemnificatory valuation and forecast.

Multi-objective optimization of injection-molded plastic parts using entropy weight, random forest, and genetic algorithm methods

2020 ◽  
Vol 40 (4) ◽  
pp. 360-371
Author(s):  
Yanli Cao ◽  
Xiying Fan ◽  
Yonghuan Guo ◽  
Sai Li ◽  
Haiyue Huang
Keyword(s):  
Genetic Algorithm ◽  
Random Forest ◽  
Regression Model ◽  
Process Parameters ◽  
Multi Objective Optimization ◽  
Entropy Weight ◽  
Multi Objective ◽  
Injection Molded ◽  
Plastic Parts

AbstractThe qualities of injection-molded parts are affected by process parameters. Warpage and volume shrinkage are two typical defects. Moreover, insufficient or excessively large clamping force also affects the quality of parts and the cost of the process. An experiment based on the orthogonal design was conducted to minimize the above defects. Moldflow software was used to simulate the injection process of each experiment. The entropy weight was used to determine the weight of each index, the comprehensive evaluation value was calculated, and multi-objective optimization was transformed into single-objective optimization. A regression model was established by the random forest (RF) algorithm. To further illustrate the reliability and accuracy of the model, back-propagation neural network and kriging models were taken as comparative algorithms. The results showed that the error of RF was the smallest and its performance was the best. Finally, genetic algorithm was used to search for the minimum of the regression model established by RF. The optimal parameters were found to improve the quality of plastic parts and reduce the energy consumption. The plastic parts manufactured by the optimal process parameters showed good quality and met the requirements of production.

Use of Statistical Techniques to Synthesize Explicit Criteria Developed by an Expert Panel

2006 ◽  
Vol 45 (06) ◽  
pp. 622-630 ◽  
Author(s):  
J. M. Quintana ◽  
A. Urkaregi ◽  
I. Arostegui
Keyword(s):  
Clinical Decision Making ◽  
Expert Panel ◽  
Multiple Correspondence Analysis ◽  
Classification Tree ◽  
Regression Tree ◽  
Clinical Decision ◽  
Statistical Techniques ◽  
Hip Joint Replacement ◽  
Weighted Probability ◽  
Explicit Criteria

Summary Objectives: Methodology based on expert panels has been commonly used to evaluate the appropriateness of interventions. An important issue is the adequate synthesis of the generated information in an applicable way to clinical decision making. This paper shows how statistical procedures help synthesize the results of an expert panel. Methods: Three statistical techniques were applied to an expert panel that developed explicit criteria to assess the appropriateness of total hip joint replacement: classification tree, regression tree and multiple correspondence analysis combined with automatic classification. Results: Results provided by the three models were shown in graphical displays and were compared to the original panel results using crude and weighted probability of misclassification. Results were also applied to real interventions in order to know the implication of the misclassification on real patients. Conclusions: The statistical techniques help summarize data from panels of experts and provide useful decision models for clinical practice, especially when the number of indications is big. However, degree of misclassification and its implication should be taken into account.

scholarly journals NCOG-31. DEVELOPMENT AND EVALUATION OF PROGNOSTIC MODELS TO PREDICT SURVIVAL OF PATIENTS WITH GLIOBLASTOMA MULTIFORME

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii135-ii136
Author(s):  
John Lin ◽  
Michelle Mai ◽  
Saba Paracha
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Glioblastoma Multiforme ◽  
Random Forest ◽  
Regression Model ◽  
Clinical Decision Support ◽  
Multivariate Regression ◽  
Visual Analysis ◽  
Clinical Decision ◽  
Prognostic Models

Abstract Glioblastoma multiforme (GBM), the most common form of glioma, is a malignant tumor with a high risk of mortality. By providing accurate survival estimates, prognostic models have been identified as promising tools in clinical decision support. In this study, we produced and validated two machine learning-based models to predict survival time for GBM patients. Publicly available clinical and genomic data from The Cancer Genome Atlas (TCGA) and Broad Institute GDAC Firehouse were obtained through cBioPortal. Random forest and multivariate regression models were created to predict survival. Predictive accuracy was assessed and compared through mean absolute error (MAE) and root mean square error (RMSE) calculations. 619 GBM patients were included in the dataset. There were 381 (62.9%) cases of recurrence/progression and 53 (8.7%) cases of disease-free survival. The MAE and RMSE values were 0.553 and 0.887 years respectively for the random forest regression model, and they were 1.756 and 2.451 years respectively for the multivariate regression model. Both models accurately predicted overall survival. Comparison of models through MAE, RMSE, and visual analysis produced higher accuracy values for random forest than multivariate linear regression. Further investigation on feature selection and model optimization may improve predictive power. These findings suggest that using machine learning in GBM prognostic modeling will improve clinical decision support. *Co-first authors.

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