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Journal articles on the topic 'Bagging Forest'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

Jatmiko, Yogo Aryo, Septiadi Padmadisastra, and Anna Chadidjah. "ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASI." MEDIA STATISTIKA 12, no. 1 (2019): 1. http://dx.doi.org/10.14710/medstat.12.1.1-12.

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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 determi

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Ibrahim, Muhammad. "Evolution of Random Forest from Decision Tree and Bagging: A Bias-Variance Perspective." Dhaka University Journal of Applied Science and Engineering 7, no. 1 (2023): 66–71. http://dx.doi.org/10.3329/dujase.v7i1.62888.

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The ensemble methods are one of the most heavily used techniques in machine learning. The random forest arguably spearheads this army of learners. Being sprung from the decision tree in the late 90s, the benefits of a random forest have rightfully attracted practitioners to widely and successfully apply this powerful yet simple-to-understand technique to numerous applications. In this study we explain the evolution of a random forest from a decision tree in the context of bias and variance of learning theory. While doing so, we focus on the interplay between the correlation and generalization

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Ali, Amir, Purwanto Purwanto, and Mundakir Mundakir. "Increasing the Accuracy of Random Forest Algorithm Using Bagging Techniques in Cases of Stunting Toddlers." Jurnal Sistem Informasi Bisnis 15, no. 2 (2025): 167–72. https://doi.org/10.14710/vol15iss2pp167-172.

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Increasing the accuracy value can be increased by using other algorithms. Increasing the accuracy value of a classification algorithm, the level of success of the algorithm's prediction is more precise and appropriate in providing its label. The purpose of the research is look performance of accuracy value for prediction with bagging algorithm. This research use random forest algorithm and bagging algorithm used for optimization. 12 data whose position is far from other data. 12 data deviate from the data pattern and are outliers. With z-score process, it will be processed to eliminate outlier

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Hadi, Dhea Agustina, and Dwi Agustin Nuriani Sirodj. "Metode Random Forest untuk Klasifikasi Penyakit Diabetes." Bandung Conference Series: Statistics 3, no. 2 (2023): 428–35. http://dx.doi.org/10.29313/bcss.v3i2.8354.

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Abstract. Random Forest is a supervised learning algorithm developed from decision trees with the application of boostrap aggregating (bagging). This method grows trees from decision trees to produce a forest or the best model called the random forest model. Tree growth is done with randomly selected data with returns through the bagging process. Random forest is considered to provide better performance results for diabetes data among other supervised learning methods, because random forest and has the lowest error rate compared to other methods. Random forest is also an important technique fo

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Khumaidi, Ali, Risanto Darmawan, and Diajeng Reztrianti. "Application of Ensemble Tree Algorithm for Installment Payment Arrears Prediction at Makmur Bersama Credit Union." Faktor Exacta 17, no. 2 (2024): 161. http://dx.doi.org/10.30998/faktorexacta.v17i2.21819.

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<span lang="EN-US">One of the widely used machine learning techniques is the ensemble tree method, which is a combination of several classification trees where the final decision is based on the combined predictions of each tree. This approach produces better accuracy than a single classification tree. Two common methods used in the ensemble tree technique are boosting and bagging. This research will predict the status of installment payments at CU Makmur Bersama Credit Union. The method used is the bagging tree method, namely random forest and boosting, namely AdaBoost. To get optimal r

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Tuysuzoglu, Goksu, and Derya Birant. "Enhanced Bagging (eBagging): A Novel Approach for Ensemble Learning." International Arab Journal of Information Technology 17, no. 4 (2020): 515–28. http://dx.doi.org/10.34028/iajit/17/4/10.

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Bagging is one of the well-known ensemble learning methods, which combines several classifiers trained on different subsamples of the dataset. However, a drawback of bagging is its random selection, where the classification performance depends on chance to choose a suitable subset of training objects. This paper proposes a novel modified version of bagging, named enhanced Bagging (eBagging), which uses a new mechanism (error-based bootstrapping) when constructing training sets in order to cope with this problem. In the experimental setting, the proposed eBagging technique was tested on 33 well

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Novikova, Tatyana, Svetlana Evdokimova, and Gotsui Wu. "Development of a quantitative investment algorithm based on Random Forest." Modeling of systems and processes 15, no. 4 (2022): 53–60. http://dx.doi.org/10.12737/2219-0767-2022-15-4-53-60.

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In modern research of the stock market, specialists and scientists are improving algorithms and models, combining them with each other, with strategies and market conditions for stock selection. This paper presents an overview of stock selection models for quantitative investment, which was the basis for the proposed procedure and algorithm of quantitative investment, which allow modeling the investment process. The developed algorithm is based on the CART decision tree and Random Forest, which includes the bagging algorithm. The bagging algorithm divides the training set into several new trai

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Anouze, Abdel Latef M., and Imad Bou-Hamad. "Data envelopment analysis and data mining to efficiency estimation and evaluation." International Journal of Islamic and Middle Eastern Finance and Management 12, no. 2 (2019): 169–90. http://dx.doi.org/10.1108/imefm-11-2017-0302.

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PurposeThis paper aims to assess the application of seven statistical and data mining techniques to second-stage data envelopment analysis (DEA) for bank performance.Design/methodology/approachDifferent statistical and data mining techniques are used to second-stage DEA for bank performance as a part of an attempt to produce a powerful model for bank performance with effective predictive ability. The projected data mining tools are classification and regression trees (CART), conditional inference trees (CIT), random forest based on CART and CIT, bagging, artificial neural networks and their st

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Krautenbacher, Norbert, Fabian J. Theis, and Christiane Fuchs. "Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies." Computational and Mathematical Methods in Medicine 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/7847531.

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Epidemiological studies often utilize stratified data in which rare outcomes or exposures are artificially enriched. This design can increase precision in association tests but distorts predictions when applying classifiers on nonstratified data. Several methods correct for this so-called sample selection bias, but their performance remains unclear especially for machine learning classifiers. With an emphasis on two-phase case-control studies, we aim to assess which corrections to perform in which setting and to obtain methods suitable for machine learning techniques, especially the random for

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Kotsiantis, Sotiris. "Combining bagging, boosting, rotation forest and random subspace methods." Artificial Intelligence Review 35, no. 3 (2010): 223–40. http://dx.doi.org/10.1007/s10462-010-9192-8.

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Anjum, Madiha, Kaffayatullah Khan, Waqas Ahmad, Ayaz Ahmad, Muhammad Nasir Amin, and Afnan Nafees. "Application of Ensemble Machine Learning Methods to Estimate the Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete." Polymers 14, no. 18 (2022): 3906. http://dx.doi.org/10.3390/polym14183906.

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In this study, compressive strength (CS) of fiber-reinforced nano-silica concrete (FRNSC) was anticipated using ensemble machine learning (ML) approaches. Four types of ensemble ML methods were employed, including gradient boosting, random forest, bagging regressor, and AdaBoost regressor, to achieve the study’s aims. The validity of employed models was tested and compared using the statistical tests, coefficient of determination (R2), and k-fold method. Moreover, a Shapley Additive Explanations (SHAP) analysis was used to observe the interaction and effect of input parameters on the CS of FRN

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Muhathir, Muhathir. "Compares the effectiveness of the bagging method in classifying spices using the histogram of oriented gradient feature extraction technique." Jurnal Teknik Informatika C.I.T Medicom 15, no. 1 (2023): 48–57. http://dx.doi.org/10.35335/cit.vol15.2023.386.pp48-57.

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Spice classification is a crucial task in the food industry to ensure food safety and quality. This study focuses on the classification of spices using the Histogram of Oriented Gradient (HoG) feature extraction method and bagging method. The objective of this research is to compare the performance of three different models of bagging method, including Bootstrap Aggregating (Bagging), Random Forests, and Extra Tree Classifier, in classifying spices. The evaluation metrics used in this research are Precision, Recall, F1-Score, F2-Score, Jaccard Score, and Accuracy. The results show that the Ran

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Srivastava, A. K., D. Singh, A. S. Pandey, and Sanjay Kumar. "SHORT TERM PRICE FORECASTING USING TREE BASED METHODS." COMPUSOFT: An International Journal of Advanced Computer Technology 08, no. 01 (2019): 2985–89. https://doi.org/10.5281/zenodo.14811238.

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In this paper, electricity price forecasting using J48, Random forest and Bagging are used to effectively forecast the electricity price. These models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. The effectiveness of the proposed methods has been validated through comprehensive tests using real price data from Australian electricity market. The comparison of these methods shows that the bagging is having an edge as the accuracy is concerned. 

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Husni Odeh, Omar, Anas Arram, Ahmed Almassri, and Murad Njoum. "Classification of Spam URLs Using Machine Learning Approaches." Israa University Journal for Applied Science 8, no. 1 (2025): 1–17. https://doi.org/10.52865/bemw4489.

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Background:The Internet is used by billions of users every day because it offers fast and free communication tools and platforms. However, with this significant increase in usage, huge amounts of spam are generated every second, which wastes internet resources and, more importantly, users’ time. Objective:This study investigates the use of machine learning models to classify URLs as spam or non-spam. Methods: We first extract the features from the URL as it has only one feature, and then we compare the performance of several models, including k-nearest neighbors, bagging, random forest, logist

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Li, Qingfu, and Ao Xu. "Concrete Carbonization Prediction Method Based on Bagging and Boosting Fusion Framework." Buildings 15, no. 8 (2025): 1349. https://doi.org/10.3390/buildings15081349.

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Concrete carbonation is an important factor causing corrosion of steel reinforcement, which leads to damage to reinforced concrete structures. To address the problem of concrete carbonation depth prediction, this paper proposes a prediction model. The framework synergistically integrates Bagging and Boosting algorithms, specifically replacing the original Random Forest base learner with gradient Boosting variants (LightGBM (version 4.1.0), XGBoost (version 2.1.1), and CatBoost (version 1.2.5)). This hybrid approach exploits the strengths of all three algorithms to reduce variance and bias, and

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Kwenda, Clopas, Mandlenkosi Gwetu, and Jean Vincent Fonou-Dombeu. "Ontology with Deep Learning for Forest Image Classification." Applied Sciences 13, no. 8 (2023): 5060. http://dx.doi.org/10.3390/app13085060.

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Most existing approaches to image classification neglect the concept of semantics, resulting in two major shortcomings. Firstly, categories are treated as independent even when they have a strong semantic overlap. Secondly, the features used to classify images into different categories can be the same. It has been demonstrated that the integration of ontologies and semantic relationships greatly improves image classification accuracy. In this study, a hybrid ontological bagging algorithm and an ensemble technique of convolutional neural network (CNN) models have been developed to improve fores

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Juwariyem, Juwariyem, Sriyanto Sriyanto, Sri Lestari, and Chairani Chairani. "Prediction of Stunting in Toddlers Using Bagging and Random Forest Algorithms." Sinkron 8, no. 2 (2024): 947–55. http://dx.doi.org/10.33395/sinkron.v8i2.13448.

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Stunting is a condition of failure to thrive in toddlers. This is caused by lack of nutrition over a long period of time, exposure to repeated infections, and lack of stimulation. This malnutrition condition is influenced by the mother's health during pregnancy, the health status of adolescents, as well as the economy and culture and the environment, such as sanitation and access to health services. To find out predictions of stunting, currently we still use a common method, namely Secondary Data Analysis, namely by conducting surveys and research to collect data regarding stunting. This data

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Maulana, Muhammad Naufal, Muljono Muljono, and Eka Putra Agus Meindiawan. "Comparative Analysis of Homogeneous and Heterogeneous Ensembles for Diabetes Classification Optimization." sinkron 9, no. 1 (2025): 512–21. https://doi.org/10.33395/sinkron.v9i1.14439.

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Diabetes mellitus is a chronic disease with an increasing prevalence worldwide, including in Indonesia, reaching 11.7% by 2023. Early prediction of this disease is essential for more effective management. This study aims to develop a diabetes mellitus prediction model using an ensemble learning approach, including homogeneous (boosting and bagging) and heterogeneous (stacking and blending) techniques. In this study, the boosting algorithm using AdaBoost with Random Forest as the base estimator showed the highest accuracy of 98%, with balanced precision and recall. The bagging technique, which

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Putri, Elita Rizkiani, and Dede Brahma Arianto. "Perbandingan Performa Algoritma Metode Bagging dan Boosting pada Prediksi Konsentrasi PM10 di Jakarta Utara." Jurnal Nasional Teknologi dan Sistem Informasi 10, no. 1 (2024): 72–81. http://dx.doi.org/10.25077/teknosi.v10i1.2024.72-81.

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Jakarta Utara merupakan salah satu wilayah di DKI Jakarta yang mengalami peningkatan hari dengan kualitas udara berkategori tidak sehat, yakni 21 hari pada tahun 2017 menjadi 117 hari di 2018, tetapi kemudian menurun menjadi 45 hari pada tahun 2019. Kategori tidak sehat tersebut dipengaruhi oleh polusi udara. Salah satu polutan yang ada di udara adalah PM10. Saat ini, kualitas udara dapat diprediksi menggunakan pendekatan algoritma machine learning. Contoh metode machine learning yang terkenal adalah Metode Bagging dan Boosting yang ada di Metode Ensemble. Contoh algoritma dengan Metode Baggin

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Neo, Puay Keong, Yew Wei Leong, Moi Fuai Soon, Qing Sheng Goh, Supaphorn Thumsorn, and Hiroshi Ito. "Development of a Machine Learning Model to Predict the Color of Extruded Thermoplastic Resins." Polymers 16, no. 4 (2024): 481. http://dx.doi.org/10.3390/polym16040481.

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The conventional method for the color-matching process involves the compounding of polymers with pigments and then preparing plaques by using injection molding before measuring the color by an offline spectrophotometer. If the color fails to meet the L*, a*, and b* standards, the color-matching process must be repeated. In this study, the aim is to develop a machine learning model that is capable of predicting offline color using data from inline color measurements, thereby significantly reducing the time that is required for the color-matching process. The inline color data were measured usin

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Irawan, Devi, Eza Budi Perkasa, Yurindra Yurindra, Delpiah Wahyuningsih, and Ellya Helmud. "Perbandingan Klassifikasi SMS Berbasis Support Vector Machine, Naive Bayes Classifier, Random Forest dan Bagging Classifier." Jurnal Sisfokom (Sistem Informasi dan Komputer) 10, no. 3 (2021): 432–37. http://dx.doi.org/10.32736/sisfokom.v10i3.1302.

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Short message service (SMS) adalah salah satu media komunikasi yang penting untuk mendukung kecepatan pengunaan ponsel oleh pengguna. Sistem hibrid klasifikasi SMS digunakan untuk mendeteksi sms yang dianggap sampah dan benar. Dalam penelitian ini yang diperlukan adalah mengumpulan dataset SMS, pemilihan fitur, prapemrosesan, pembuatan vektor, melakukan penyaringan dan pembaharuan sistem. Dua jenis klasifikasi SMS pada ponsel saat ini ada yang terdaftar sebagai daftar hitam (ditolak) dan daftar putih (diterima). Penelitian ini menggunakan beberapa algoritma seperti support vector machine, Naïv

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Fitriyani, Fitriyani. "Implementasi Forward Selection dan Bagging untuk Prediksi Kebakaran Hutan Menggunakan Algoritma Naïve Bayes." Jurnal Nasional Teknologi dan Sistem Informasi 8, no. 1 (2022): 1–8. http://dx.doi.org/10.25077/teknosi.v8i1.2022.1-8.

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Kebakaran hutan tidak hanya menimbulkan kerusakan ekonomi dan ekologi, akan tetapi juga mengancam kehidupan manusia dengan pencemaran udara karena asap yang ditimbulkan.Tingginya angka kejadian kebakaran hutan menentukan pentingnya prediksi dilakukan. Algerian Forest Fire merupakan dataset kebakaran hutan yang digunakan dalam penelitian ini, dimana dataset ini akan diolah dengan model yang diusulkan. Dataset ini memiliki fitur-fitur yang tidak relevan dan akan mempengaruhi terhadap kinerja dari model yang diusulkan, sehingga pemilihan fitur yang relevan menggunakan Forward Selection. Metode Ba

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ŞEVGİN, Hikmet. "A comparative study of ensemble methods in the field of education: Bagging and Boosting algorithms." International Journal of Assessment Tools in Education 10, no. 3 (2023): 544–62. http://dx.doi.org/10.21449/ijate.1167705.

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This study aims to conduct a comparative study of Bagging and Boosting algorithms among ensemble methods and to compare the classification performance of TreeNet and Random Forest methods using these algorithms on the data extracted from ABİDE application in education. The main factor in choosing them for analyses is that they are Ensemble methods combining decision trees via Bagging and Boosting algorithms and creating a single outcome by combining the outputs obtained from each of them. The data set consists of mathematics scores of ABİDE (Academic Skills Monitoring and Evaluation) 2016 impl

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Abellán, Joaquín, Javier G. Castellano, and Carlos J. Mantas. "A New Robust Classifier on Noise Domains: Bagging of Credal C4.5 Trees." Complexity 2017 (2017): 1–17. http://dx.doi.org/10.1155/2017/9023970.

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The knowledge extraction from data with noise or outliers is a complex problem in the data mining area. Normally, it is not easy to eliminate those problematic instances. To obtain information from this type of data, robust classifiers are the best option to use. One of them is the application of bagging scheme on weak single classifiers. The Credal C4.5 (CC4.5) model is a new classification tree procedure based on the classical C4.5 algorithm and imprecise probabilities. It represents a type of the so-calledcredal trees. It has been proven that CC4.5 is more robust to noise than C4.5 method a

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Milijević, Jelena. "PREDIKCIJA DOBITNIKA FILMSKE NAGRADE OSKAR UPOTREBOM MAŠINSKOG UČENJA." Zbornik radova Fakulteta tehničkih nauka u Novom Sadu 40, no. 03 (2025): 129–32. https://doi.org/10.24867/30be07milijevic.

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У раду је вршена предикција добитника награде Оскар за најбољи филм и за глумачко остварење. Изтраживанје ове теме проистиче из њеног значаја za filmsku industriju. Korišćeni algoritmi su: Logistička Regresija, SVM, Random Forest, Bagging, XGBoost i Неуронска Мрежа. За евалуацију модела коришћена је 10-унакрсна валидација. У првој предикцији најбоље се показао Random Forest са таћчошћу 91,59%, а у другој XGBoost са 84,39%.

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Hassan, Hassan, Fatma Sakr, Fadi yassin Salem Al jawazneh, Mutasem K. Alsmadi, Ibrahim A. Gomaa, and Shaimaa Abdallah. "Data-DrivenWeather Prediction: Integrating Deep Learning and Ensemble Models for Robust Weather Forecasting." Journal of Cybersecurity and Information Management 15, no. 2 (2025): 260–84. https://doi.org/10.54216/jcim.150220.

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Accurate weather forecasting is critical for sectors like agriculture, transportation, disaster management, and public safety. This paper presents a comprehensive methodology integrating traditional machine learning models, deep learning techniques, and ensemble learning approaches to enhance the precision and reliability of weather predictions. Using a combination of four datasets—two for classification and two for regression—the study evaluates various machine learning models such as Decision Trees, Support Vector Machines, and KNearest Neighbors, alongside ensemble methods like Bagging and

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Alfaiad, Majdi Ameen, Kaffayatullah Khan, Waqas Ahmad, Muhammad Nasir Amin, Ahmed Farouk Deifalla, and Nivin A. Ghamry. "Evaluating the compressive strength of glass powder-based cement mortar subjected to the acidic environment using testing and modeling approaches." PLOS ONE 18, no. 4 (2023): e0284761. http://dx.doi.org/10.1371/journal.pone.0284761.

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This study conducted experimental and machine learning (ML) modeling approaches to investigate the impact of using recycled glass powder in cement mortar in an acidic environment. Mortar samples were prepared by partially replacing cement and sand with glass powder at various percentages (from 0% to 15%, in 2.5% increments), which were immersed in a 5% sulphuric acid solution. Compressive strength (CS) tests were conducted before and after the acid attack for each mix. To create ML-based prediction models, such as bagging regressor and random forest, for the CS prediction following the acid at

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Rahaman, Mustafizur, Ekramul Hasan, DIPTA PAUL, Md Al Amin, and Md Tuhin Mia. "Early Detection of Breast Cancer Using Machine Learning: A Tool for Enhanced Clinical Decision Support." British Journal of Nursing Studies 5, no. 1 (2025): 55–63. https://doi.org/10.32996/bjns.2025.5.1.6.

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Breast cancer arises when there is an abnormal increase in breast tissue, resulting in the creation of lumps or irregular cell layers. This cancer ranks as the second most common among women worldwide, trailing only melanoma, and primarily impacts those over 50 years old, although it can manifest at any age. Timely identification and robust preventive measures are essential for minimizing health risks associated with cancer. Clinical trials in cancer prevention are persistently investigating innovative approaches for early diagnosis and treatment. This research utilizes machine learning method

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Yin, Liwen. "Forecasting Sector Rotation of A-share Market Using LSTM and Random Forest." Advances in Economics, Management and Political Sciences 49, no. 1 (2023): 109–23. http://dx.doi.org/10.54254/2754-1169/49/20230493.

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To improve the efficacy of stock prediction strategies, researching sector rotation is essential. This study addresses the sector rotation problem in the A-share market and proposes an approach that leverages LSTM and random forest models to forecast sector rotation trends. Extensive evaluations are conducted to assess the models' prediction accuracy, comparing different evaluation indicators. The random search algorithm is employed to optimize model parameters, while the adaptive learning rate Adam algorithm is utilized to enhance convergence performance. The final experimental results demons

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Gholamrezaie, Faezeh, Arash Hosseini, and Nigar Ismayilova. "A COMPARATIVE ASSESSMENT OF MACHINE LEARNING MODELS FOR PREDICTING WIND SPEED." Azerbaijan Journal of High Performance Computing 5, no. 2 (2022): 57–71. http://dx.doi.org/10.32010/26166127.2022.5.1.57.71.

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Renewable energy is one of the most critical issues of continuously increasing electricity consumption which is becoming a desirable alternative to traditional methods of electricity generation such as coal or fossil fuels. This study aimed to develop, evaluate, and compare the performance of Linear multiple regression (MLR), support vector regression (SVR), Bagging and random forest (R.F.), and decision tree (CART) models in predicting wind speed in Southeastern Iran. The data used in this research is related to the statistics of 10 minutes of wind speed in 10-meter, 30-meter, and 40-meter wi

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Rochadiani, Theresia Herlina. "Prediction of Air Quality Index Using Ensemble Models." Journal of Applied Informatics and Computing 8, no. 2 (2024): 384–89. https://doi.org/10.30871/jaic.v8i2.8532.

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The impact of air pollution on health is measured by the Air Quality Index (AQI). Accurate AQI prediction is essential for pollution reduction and public health recommendations. Traditional methods of monitoring air quality are inaccurate and time-consuming. This study uses IoT-based air quality data from Kampung Kalipaten, Tangerang to build an AQI prediction model with machine learning, specifically an ensemble model. Ensemble techniques such as bagging and boosting, which increase the reliability of predictions by reducing model bias and inconsistency, improve AQI prediction. Four ensemble

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Ziyadullaev, Davron, Dilnoz Muhamediyeva, Khosiyat Khujamkulova, Doniyor Abdurakhimov, Azizahon Maksumkhanova, and Gulchiroy Ziyodullaeva. "Ensemble data mining methods for assessing soil fertility." E3S Web of Conferences 494 (2024): 02013. http://dx.doi.org/10.1051/e3sconf/202449402013.

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The application of ensemble data mining methods in assessing soil fertility and the use of methods such as random forest, gradient boosting and bagging to determine the level of soil fertility are examined in the article. Ensemble methods combine multiple machine learning models to improve the accuracy and stability of estimates. These methods consider various factors, including soil chemistry, climatic conditions, and historical crop yield data. The study also examines the application of the decision tree algorithm and such methods as random forest and bagging to estimate soil fertility. Perf

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Choi, Sunghyeon, and Jin Hur. "An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting." Energies 13, no. 6 (2020): 1438. http://dx.doi.org/10.3390/en13061438.

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As the world is aware, the trend of generating energy sources has been changing from conventional fossil fuels to sustainable energy. In order to reduce greenhouse gas emissions, the ratio of renewable energy sources should be increased, and solar and wind power, typically, are driving this energy change. However, renewable energy sources highly depend on weather conditions and have intermittent generation characteristics, thus embedding uncertainty and variability. As a result, it can cause variability and uncertainty in the power system, and accurate prediction of renewable energy output is

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Gacto-Colorado, María José, José Manuel Soto-Hidalgo, Fernández Jesús Alcalá, and Fernández Rafael Alcalá. "Experimental Study on 164 Algorithms Available in Software Tools for Solving Standard Non-Linear Regression Problems." IEEE Access 7 (August 20, 2019): 108916–39. https://doi.org/10.5281/zenodo.10578532.

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In the specialized literature, researchers can find a large number of proposals for solving regression problems that come from different research areas. However, researchers tend to use only proposals from the area in which they are experts. This paper analyses the performance of a large number of the available regression algorithms from some of the most known and widely used software tools in order to help non-expert users from other areas to properly solve their own regression problems and to help specialized researchers developing well-founded future proposals by properly comparing and iden

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Bharathi, Dr R. "Securing IoT Devices with Advanced Cyber Defense Using Random Forest and Django." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 538–50. https://doi.org/10.22214/ijraset.2025.70207.

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Combining machine learning with the Django framework significantly enhances intrusion detection in Internet of Things (IoT) environments. This system incorporates powerful classification models Random Forest, Bagging, and Ridge to improve detection precision and resilience against cyberattacks. Random Forest utilizes multiple decision trees to accurately identify diverse and complex attack patterns across large datasets. Bagging enhances the model’s robustness by lowering variance through model aggregation, ensuring reliable performance in different intrusion scenarios. Ridge Classifier adds r

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Yoga Religia, Agung Nugroho, and Wahyu Hadikristanto. "Klasifikasi Analisis Perbandingan Algoritma Optimasi pada Random Forest untuk Klasifikasi Data Bank Marketing." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 1 (2021): 187–92. http://dx.doi.org/10.29207/resti.v5i1.2813.

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The world of banking requires a marketer to be able to reduce the risk of borrowing by keeping his customers from occurring non-performing loans. One way to reduce this risk is by using data mining techniques. Data mining provides a powerful technique for finding meaningful and useful information from large amounts of data by way of classification. The classification algorithm that can be used to handle imbalance problems can use the Random Forest (RF) algorithm. However, several references state that an optimization algorithm is needed to improve the classification results of the RF algorithm

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Pérez Rave, Jorge Iván, Favián González Echavarría, and Juan Carlos Correa Morales. "Modeling of apartment prices in a Colombian context from a machine learning approach with stable-important attributes." DYNA 87, no. 212 (2020): 63–72. http://dx.doi.org/10.15446/dyna.v87n212.80202.

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The objective of this work is to develop a machine learning model for online pricing of apartments in a Colombian context. This article addresses three aspects: i) it compares the predictive capacity of linear regression, regression trees, random forest and bagging; ii) it studies the effect of a group of text attributes on the predictive capability of the models; and iii) it identifies the more stable-important attributes and interprets them from an inferential perspective to better understand the object of study. The sample consists of 15,177 observations of real estate. The methods of assem

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SEGUÍ, SANTI, LAURA IGUAL, and JORDI VITRIÀ. "BAGGED ONE-CLASS CLASSIFIERS IN THE PRESENCE OF OUTLIERS." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 05 (2013): 1350014. http://dx.doi.org/10.1142/s0218001413500146.

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The problem of training classifiers only with target data arises in many applications where nontarget data are too costly, difficult to obtain, or not available at all. Several one-class classification methods have been presented to solve this problem, but most of the methods are highly sensitive to the presence of outliers in the target class. Ensemble methods have therefore been proposed as a powerful way to improve the classification performance of binary/multi-class learning algorithms by introducing diversity into classifiers. However, their application to one-class classification has bee

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Kashmoola, Mohammed Alaaalden, Samah Fakhri Aziz, Hasan Mudhafar Qays, and Naors Y. Anad Alsaleem. "Unbalanced credit fraud modeling based on bagging and bayesian optimization." Eastern-European Journal of Enterprise Technologies 3, no. 4 (123) (2023): 47–53. http://dx.doi.org/10.15587/1729-4061.2023.279936.

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Credit fraud modeling is a crucial area of research that is highly relevant to the credit loan industry. Effective risk management is a key factor in providing quality credit services and directly impacts the profitability and bad debt ratio of leading organizations in this sector. However, when the distribution of credit fraud data is highly unbalanced, it can lead to noise errors caused by information distortion, periodic statistical errors, and model biases during training. This can cause unfair results for the minority class (target class) and increase the risk of overfitting. While tradit

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Kapil, Divya. "Enhancing MNIST Digit Recognition with Ensemble Learning Techniques." Mathematical Statistician and Engineering Applications 70, no. 2 (2021): 1362–71. http://dx.doi.org/10.17762/msea.v70i2.2328.

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    Abstract The classification task known as MNIST digit recognition involves identifying handwritten numbers into their corresponding values. Although there are numerous approaches proposed for this type of task, they typically face issues in achieving high accuracy. One method that can improve single models' performance is through ensemble learning. The goal of this study is to explore the use of various learning techniques, such as boosting and bagging, in combination with random forest models and decision trees, to improve the performance of MNIST digit recog

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Primantara, Ari, Udisubakti Ciptomulyono, and Berlian Al Kindhi. "Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry." AgriEngineering 7, no. 6 (2025): 187. https://doi.org/10.3390/agriengineering7060187.

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Inconsistencies in product weight during fertilizer bagging can lead to material losses and reduced operational efficiency. This study investigates the use of machine learning to predict weight deviations in the Urea Bagging Unit at PT Petrokimia Gresik. Four algorithms were used: an Artificial Neural Network (ANN), Random Forest Regression (RFR), Linear Regression (LR), and Support Vector Regression (SVR). The dataset used consisted of nine numeric sensor variables. Among the models, RFR achieved the highest predictive accuracy (R2 = 0.9638, RMSE = 0.0496, MAE = 0.0338). Feature importance an

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Taqwa Prasetyaningrun, Putri, Irfan Pratama, and Albert Yakobus Chandra. "Implementation Of Machine Learning To Determine The Best Employees Using Random Forest Method." IJCONSIST JOURNALS 2, no. 02 (2021): 53–59. http://dx.doi.org/10.33005/ijconsist.v2i02.43.

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In the world of work the presence of the best employees becomes a benchmark of progress of the company itself. In the determination usually by looking at the performance of the employee e.g. from craft, discipline and also other achievements. The goal is to optimize in decision making to the best employees. Models obtained for employee predictions tested on real data sets provided by IBM analytics, which includes 29 features and about 22005 samples. In this paper we try to build system that predicts employee attribution based on A collection of employee data from kaggle website. We have used f

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Lingga Dewi, Adhe, and Muhamad Akrom. "Investigation of an amino acid compound as a corrosion inhibitor via ensemble learning." Journal of Multiscale Materials Informatics 1, no. 2 (2024): 14–18. http://dx.doi.org/10.62411/jimat.v1i2.11053.

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In this study, we evaluate the performance of various machine learning models, including Random Forest (RF), Bagging (BAG), AdaBoost (ADA), Artificial Neural Network (ANN), and Support Vector Machine (SVM), using metrics such as R², Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results indicate that AdaBoost (ADA) achieves the highest performance with an R² of 0.999, RMSE of 2.32, and MAE of 2.24, making it the most accurate model with the smallest prediction errors. Bagging (BAG) also performs exceptionally well, with an R2 of 0.996, RMSE of 3.09, and MAE of 2.92. The Art

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Patel, Mehul, Mittal Chavda, Rajesh Patel, Ankur Goswami, and Jayesh Mevada. "Predicting Landslide Using Machine Learning Techniques." ITM Web of Conferences 65 (2024): 03012. http://dx.doi.org/10.1051/itmconf/20246503012.

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In mountainous areas prone to landslides, it’s crucial to map out where these hazardous events are likely to occur to mitigate risks effectively. This study focuses employing an integrated approach to assess landslide susceptibility using Random Forest (RF), Stacking, Vote, AdaBoostM1, and Bagging. 13 factors influencing landslide occurrence are identified for modeling purposes. To evaluate and compare the models’ performance, multiple statistical methods are employed. The analysis highlights the effectiveness of employing machine learning models, Random Forest (RF), Stacking, Bagging, and Vot

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Hannan, Abdul, and Jagadeesh Anmala. "Classification and Prediction of Fecal Coliform in Stream Waters Using Decision Trees (DTs) for Upper Green River Watershed, Kentucky, USA." Water 13, no. 19 (2021): 2790. http://dx.doi.org/10.3390/w13192790.

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The classification of stream waters using parameters such as fecal coliforms into the classes of body contact and recreation, fishing and boating, domestic utilization, and danger itself is a significant practical problem of water quality prediction worldwide. Various statistical and causal approaches are used routinely to solve the problem from a causal modeling perspective. However, a transparent process in the form of Decision Trees is used to shed more light on the structure of input variables such as climate and land use in predicting the stream water quality in the current paper. The Dec

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S., Vikas, and Thimmaraju S.N. "Enhancement of Data Classification Accuracy using Bagging Technique in Random Forest." International Journal of Computer Sciences and Engineering 7, no. 8 (2019): 185–88. http://dx.doi.org/10.26438/ijcse/v7i8.185188.

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Tao, Xuanyi. "A random forest-based prediction of cardiovascular diseases." Applied and Computational Engineering 76, no. 1 (2024): 311–16. http://dx.doi.org/10.54254/2755-2721/76/20240625.

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With the rapid development of human arithmetic and new algorithms the use of machine learning in the healthcare industry is growing rapidly. A cardiovascular dataset is used to explore the correlation between various data in the dataset and cardiovascular diseases. Five types of machine learning algorithms, namely logistic regression, Adaboost, decision tree classifier, random forest, and neural network, are used to predict cardiovascular diseases. After training, it can efficiently process input samples with high-dimensional features, and the Random Forest model, which integrates multiple tre

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Adnan, A., A. M. Yolanda, and F. Natasya. "A Comparison of Bagging and Boosting on Classification Data: Case Study on Rainfall Data in Sultan Syarif Kasim II Meteorological Station in Pekanbaru." Journal of Physics: Conference Series 2049, no. 1 (2021): 012053. http://dx.doi.org/10.1088/1742-6596/2049/1/012053.

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Abstract A frequent way for classification data is using a machine learning algorithm alongside ensemble methods like bagging and boosting. In earlier studies, these two algorithms have shown to be very accurate. The aim of this research is to discover performance of bagging and boosting to classify rainfall data obtained at the Sultan Syarif Kasim II Meteorological Station in Pekanbaru from 1 January 2018 until 31 July 2021. Rainfall data are classified into two categories: rainy and non-rainy. The parameters are average temperature, average humidity, sunshine duration, wind direction at maxi

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Darji, Pinesh Arvindbhai. "Utilizing an Ensemble of Extra Tree Model for Classifying Mesothelioma Cancer." African Journal of Biological Sciences 6, no. 12 (2024): 535–45. http://dx.doi.org/10.48047/afjbs.6.12.2024.535-545.

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Objectives: Explore the potential of ensemble learning techniques like Bagging Tree, Random Forest, and Ensemble Extra Tree in transforming mesothelioma diagnosis.Overcome challenges associated with late-stage detection and limited treatment options using advanced machine learning algorithms.Enhance predictive power and feature extraction capabilities through the combination of diverse ensemble algorithms.

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Cai, Xinyue, Qinyu Jin, and Wenyu Zhang. "Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model." Science Journal of Applied Mathematics and Statistics 12, no. 5 (2024): 72–79. http://dx.doi.org/10.11648/j.sjams.20241205.11.

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For the development of the national economy, transportation is strategically significant. As the increasing ownership of automobiles, traffic jams are a common occurrence. Accurate prediction of traffic flow contributes to diverting traffic effectively and improving the quality of urban traffic, in turn improving the operation of the overall transportation system. The rapid development of artificial intelligence technologies, especially machine learning and deep learning, has provided effective methods for accurate prediction of traffic flow. Based on the above, in order to improve the accurac

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