Random Forest and Ensemble Methods

2020 ◽  
pp. 661-672
Author(s):  
George Stavropoulos ◽  
Robert van Voorstenbosch ◽  
Frederik-Jan van Schooten ◽  
Agnieszka Smolinska
Keyword(s):  
Random Forest ◽  
Ensemble Methods

Automatic Software Categorization Using Ensemble Methods and Bytecode Analysis

2017 ◽  
Vol 27 (07) ◽  
pp. 1129-1144 ◽  
Author(s):  
Cagatay Catal ◽  
Serkan Tugul ◽  
Basar Akpinar
Keyword(s):  
Random Forest ◽  
Source Code ◽  
Ensemble Methods ◽  
Support Vector ◽  
Data Sets ◽  
Class Level ◽  
Vector Machines ◽  
Vote Algorithm ◽  
Automatic Software ◽  
Closed Source

Software repositories consist of thousands of applications and the manual categorization of these applications into domain categories is very expensive and time-consuming. In this study, we investigate the use of an ensemble of classifiers approach to solve the automatic software categorization problem when the source code is not available. Therefore, we used three data sets (package level/class level/method level) that belong to 745 closed-source Java applications from the Sharejar repository. We applied the Vote algorithm, AdaBoost, and Bagging ensemble methods and the base classifiers were Support Vector Machines, Naive Bayes, J48, IBk, and Random Forests. The best performance was achieved when the Vote algorithm was used. The base classifiers of the Vote algorithm were AdaBoost with J48, AdaBoost with Random Forest, and Random Forest algorithms. We showed that the Vote approach with method attributes provides the best performance for automatic software categorization; these results demonstrate that the proposed approach can effectively categorize applications into domain categories in the absence of source code.

Predictive Modeling in Marketing: Ensemble Methods for Response Modeling

2021 ◽  
Vol 75 (3) ◽  
pp. 376-396
Author(s):  
Gabriela Alves Werb ◽  
Martin Schmidberger
Keyword(s):  
Random Forest ◽  
Predictive Modeling ◽  
Ensemble Methods ◽  
Superior Performance ◽  
Performance Improvements ◽  
Marginal Effects ◽  
Model Complex ◽  
Complex Relationships ◽  
Average Size ◽  
Data Driven Decisions

Ensemble methods have received a great deal of attention in the past years in several disciplines. One reason for their popularity is their ability to model complex relationships in large volumes of data, providing performance improvements compared to traditional methods. In this article, we implement and assess ensemble methods’ performance on a critical predictive modeling problem in marketing: predicting cross-buying behavior. The best performing model, a random forest, manages to identify 73.3 % of the cross-buyers in the holdout data while maintaining an accuracy of 72.5 %. Despite its superior performance, researchers and practitioners frequently mention the difficulty in interpreting a random forest model’s results as a substantial barrier to its implementation. We address this problem by demonstrating the usage of interpretability methods to: (i) outline the most influential variables in the model; (ii) investigate the average size and direction of their marginal effects; (iii) investigate the heterogeneity of their marginal effects; and (iv) understand predictions for individual customers. This approach enables researchers and practitioners to leverage the superior performance of ensemble methods to support data-driven decisions without sacrificing the interpretability of their results.

scholarly journals Evapotranspiration Response to Climate Change in Semi-Arid Areas: Using Random Forest as Multi-Model Ensemble Method

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 222
Author(s):  
Marcos Ruiz-Aĺvarez ◽  
Francisco Gomariz-Castillo ◽  
Francisco Alonso-Sarría
Keyword(s):  
Climate Change ◽  
Random Forest ◽  
Climate Models ◽  
Ensemble Methods ◽  
Positive Trend ◽  
Model Ensemble ◽  
Predictive Capacity ◽  
Significant Positive Trend ◽  
Hargreaves Model ◽  
Large Ensembles

Large ensembles of climate models are increasingly available either as ensembles of opportunity or perturbed physics ensembles, providing a wealth of additional data that is potentially useful for improving adaptation strategies to climate change. In this work, we propose a framework to evaluate the predictive capacity of 11 multi-model ensemble methods (MMEs), including random forest (RF), to estimate reference evapotranspiration (ET0) using 10 AR5 models for the scenarios RCP4.5 and RCP8.5. The study was carried out in the Segura Hydrographic Demarcation (SE of Spain), a typical Mediterranean semiarid area. ET0 was estimated in the historical scenario (1970–2000) using a spatially calibrated Hargreaves model. MMEs obtained better results than any individual model for reproducing daily ET0. In validation, RF resulted more accurate than other MMEs (Kling–Gupta efficiency (KGE) M=0.903, SD=0.034 for KGE and M=3.17, SD=2.97 for absolute percent bias). A statistically significant positive trend was observed along the 21st century for RCP8.5, but this trend stabilizes in the middle of the century for RCP4.5. The observed spatial pattern shows a larger ET0 increase in headwaters and a smaller increase in the coast.

scholarly journals Loan Amount Prediction Using Machine Learning

2021 ◽  
pp. 212-215
Author(s):  
Bhagyashri Rajesh Jawale ◽  
Priyanka Anil Badgujar ◽  
Rita Dnyaneshwar Talele ◽  
Dr. Dinesh D. Patil
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Research Work ◽  
Ensemble Methods ◽  
Classification Algorithm ◽  
Optimization Techniques ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Or Organization ◽  
Loan Amount

Loan amount prediction is helpful for banks or organization who want their work easier. All Banks give Loan to customer and customer first apply for loan after any bank or organization validate customer information. It must be providing some advantages for banks or company or any organization who wants to give loan. There are various methods to improve the accuracy classification algorithm. The accuracy of random forest classification algorithm can be improved using Ensemble methods. Optimization techniques and Feature selection methods available. In this research work novel hybrid feature selection algorithm using wrapper model and fisher introduced. The main objective of this paper is to prove that new hybrid model produces better accuracy than the traditional random forest algorithm.

scholarly journals Prediction of Employee Attrition Using Machine Learning and Ensemble Methods

2021 ◽  
Vol 11 (2) ◽  
pp. 110-114
Author(s):  
Aseel Qutub ◽  
◽  
Asmaa Al-Mehmadi ◽  
Munirah Al-Hssan ◽  
Ruyan Aljohani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Professional Training ◽  
Ensemble Methods ◽  
Gradient Boosting ◽  
Learning Models ◽  
Retention Strategies ◽  
Employee Attrition ◽  
The Cost ◽  
Machine Learning Models

Employees are the most valuable resources for any organization. The cost associated with professional training, the developed loyalty over the years and the sensitivity of some organizational positions, all make it very essential to identify who might leave the organization. Many reasons can lead to employee attrition. In this paper, several machine learning models are developed to automatically and accurately predict employee attrition. IBM attrition dataset is used in this work to train and evaluate machine learning models; namely Decision Tree, Random Forest Regressor, Logistic Regressor, Adaboost Model, and Gradient Boosting Classifier models. The ultimate goal is to accurately detect attrition to help any company to improve different retention strategies on crucial employees and boost those employee satisfactions.

ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASI

2019 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Yogo Aryo Jatmiko ◽  
Septiadi Padmadisastra ◽  
Anna Chadidjah
Keyword(s):  
Random Forest ◽  
Classification Tree ◽  
Ensemble Methods ◽  
Distribution Patterns ◽  
Classification Method ◽  
Simulation Data ◽  
Independent Variables ◽  
Response Data ◽  
Nonparametric Classification ◽  
Categorical Response

The conventional CART method is a nonparametric classification method built on categorical response data. Bagging is one of the popular ensemble methods whereas, Random Forests (RF) is one of the relatively new ensemble methods in the decision tree that is the development of the Bagging method. Unlike Bagging, Random Forest was developed with the idea of adding layers to the random resampling process in Bagging. Therefore, not only randomly sampled sample data to form a classification tree, but also independent variables are randomly selected and newly selected as the best divider when determining the sorting of trees, which is expected to produce more accurate predictions. Based on the above, the authors are interested to study the three methods by comparing the accuracy of classification on binary and non-binary simulation data to understand the effect of the number of sample sizes, the correlation between independent variables, the presence or absence of certain distribution patterns to the accuracy generated classification method. results of the research on simulation data show that the Random Forest ensemble method can improve the accuracy of classification.

scholarly journals Evaluation of Random Forest and Ensemble Methods at Predicting Complications Following Cardiac Surgery

2019 ◽  
pp. 376-385 ◽  
Author(s):  
Linda Lapp ◽  
Matt-Mouley Bouamrane ◽  
Kimberley Kavanagh ◽  
Marc Roper ◽  
David Young ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Random Forest ◽  
Ensemble Methods

scholarly journals Imputing Satellite-Derived Aerosol Optical Depth Using a Multi-Resolution Spatial Model and Random Forest for PM2.5 Prediction

2021 ◽  
Vol 13 (1) ◽  
pp. 126
Author(s):  
Behzad Kianian ◽  
Yang Liu ◽  
Howard H. Chang
Keyword(s):  
Random Forest ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Ensemble Methods ◽  
Predictive Performance ◽  
Spatial Prediction ◽  
Gap Filling ◽  
Environmental Health Research ◽  
Random Forest Models ◽  
Pollution Exposure

A task for environmental health research is to produce complete pollution exposure maps despite limited monitoring data. Satellite-derived aerosol optical depth (AOD) is frequently used as a predictor in various models to improve PM2.5 estimation, despite significant gaps in coverage. We analyze PM2.5 and AOD from July 2011 in the contiguous United States. We examine two methods to aid in gap-filling AOD: (1) lattice kriging, a spatial statistical method adapted to handle large amounts data, and (2) random forest, a tree-based machine learning method. First, we evaluate each model’s performance in the spatial prediction of AOD, and we additionally consider ensemble methods for combining the predictors. In order to accurately assess the predictive performance of these methods, we construct spatially clustered holdouts to mimic the observed patterns of missing data. Finally, we assess whether gap-filling AOD through one of the proposed ensemble methods can improve prediction of PM2.5 in a random forest model. Our results suggest that ensemble methods of combining lattice kriging and random forest can improve AOD gap-filling. Based on summary metrics of performance, PM2.5 predictions based on random forest models were largely similar regardless of the inclusion of gap-filled AOD, but there was some variability in daily model predictions.

scholarly journals Analysis of Heart Disease Using Parallel and Sequential Ensemble Methods With Feature Selection Techniques

2021 ◽  
Vol 6 (1) ◽  
pp. 40-56
Author(s):  
Dhyan Chandra Yadav ◽  
Saurabh Pal
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Ensemble Methods ◽  
Machine Learning Algorithms ◽  
Ensemble Method ◽  
Gradient Boosting ◽  
High Classification Accuracy

This paper has organized a heart disease-related dataset from UCI repository. The organized dataset describes variables correlations with class-level target variables. This experiment has analyzed the variables by different machine learning algorithms. The authors have considered prediction-based previous work and finds some machine learning algorithms did not properly work or do not cover 100% classification accuracy with overfitting, underfitting, noisy data, residual errors on base level decision tree. This research has used Pearson correlation and chi-square features selection-based algorithms for heart disease attributes correlation strength. The main objective of this research to achieved highest classification accuracy with fewer errors. So, the authors have used parallel and sequential ensemble methods to reduce above drawback in prediction. The parallel and serial ensemble methods were organized by J48 algorithm, reduced error pruning, and decision stump algorithm decision tree-based algorithms. This paper has used random forest ensemble method for parallel randomly selection in prediction and various sequential ensemble methods such as AdaBoost, Gradient Boosting, and XGBoost Meta classifiers. In this paper, the experiment divides into two parts: The first part deals with J48, reduced error pruning and decision stump and generated a random forest ensemble method. This parallel ensemble method calculated high classification accuracy 100% with low error. The second part of the experiment deals with J48, reduced error pruning, and decision stump with three sequential ensemble methods, namely AdaBoostM1, XG Boost, and Gradient Boosting. The XG Boost ensemble method calculated better results or high classification accuracy and low error compare to AdaBoostM1 and Gradient Boosting ensemble methods. The XG Boost ensemble method calculated 98.05% classification accuracy, but random forest ensemble method calculated high classification accuracy 100% with low error.

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