Relevant bibliographies by topics / Bagging Forest

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Bagging Forest'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Bagging Forest"

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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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Dissertations / Theses on the topic "Bagging Forest"

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Rosales, Martínez Octavio. "Caracterización de especies en plasma frío mediante análisis de espectroscopia de emisión óptica por técnicas de Machine Learning." Tesis de maestría, Universidad Autónoma del Estado de México, 2020. http://hdl.handle.net/20.500.11799/109734.

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La espectroscopía de emisión óptica es una técnica que permite la identificación de elementos químicos usando el espectro electromagnético que emite un plasma. Con base en la literatura. tiene aplicaciones diversas, por ejemplo: en la identificación de entes estelares, para determinar el punto final de los procesos de plasma en la fabricación de semiconductores o bien, específicamente en este trabajo, se tratan espectros para la determinación de elementos presentes en la degradación de compuestos recalcitrantes. En este documento se identifican automáticamente espectros de elementos tales como
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Булах, В. А., Л. О. Кіріченко, and Т. А. Радівілова. "Classification of Multifractal Time Series by Decision Tree Methods." Thesis, КНУ, 2018. http://openarchive.nure.ua/handle/document/5840.

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The article considers classification task of model fractal time series by the methods of machine learning. To classify the series, it is proposed to use the meta algorithms based on decision trees. To modeling the fractal time series, binomial stochastic cascade processes are used. Classification of time series by the ensembles of decision trees models is carried out. The analysis indicates that the best results are obtained by the methods of bagging and random forest which use regression trees.
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Assareh, Amin. "OPTIMIZING DECISION TREE ENSEMBLES FOR GENE-GENE INTERACTION DETECTION." Kent State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=kent1353971575.

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Yang, Kaolee. "A Statistical Analysis of Medical Data for Breast Cancer and Chronic Kidney Disease." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1587052897029939.

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Zoghi, Zeinab. "Ensemble Classifier Design and Performance Evaluation for Intrusion Detection Using UNSW-NB15 Dataset." University of Toledo / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596756673292254.

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Ulriksson, Marcus, and Shahin Armaki. "Analys av prestations- och prediktionsvariabler inom fotboll." Thesis, Uppsala universitet, Statistiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-324983.

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Uppsatsen ämnar att försöka förklara hur olika variabler angående matchbilden i en fotbollsmatch påverkar slutresultatet. Dessa variabler är uppdelade i prestationsvariabler och kvalitétsvariabler. Prestationsvariablerna är baserade på prestationsindikatorer inspirerat av Hughes och Bartlett (2002). Kvalitétsvariablerna förklarar hur bra de olika lagen är. Som verktyg för att uppnå syftet används olika klassificeringsmodeller utifrån både prestationsvariablerna och kvalitétsvariablerna. Först undersöktes vilka prestationsindikatorer som var viktigast. Den bästa modellen klassificerade cirka 60
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Alsouda, Yasser. "An IoT Solution for Urban Noise Identification in Smart Cities : Noise Measurement and Classification." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-80858.

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Noise is defined as any undesired sound. Urban noise and its effect on citizens area significant environmental problem, and the increasing level of noise has become a critical problem in some cities. Fortunately, noise pollution can be mitigated by better planning of urban areas or controlled by administrative regulations. However, the execution of such actions requires well-established systems for noise monitoring. In this thesis, we present a solution for noise measurement and classification using a low-power and inexpensive IoT unit. To measure the noise level, we implement an algorithm for
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Thorén, Daniel. "Radar based tank level measurement using machine learning : Agricultural machines." Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176259.

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Agriculture is becoming more dependent on computerized solutions to make thefarmer’s job easier. The big step that many companies are working towards is fullyautonomous vehicles that work the fields. To that end, the equipment fitted to saidvehicles must also adapt and become autonomous. Making this equipment autonomoustakes many incremental steps, one of which is developing an accurate and reliable tanklevel measurement system. In this thesis, a system for tank level measurement in a seedplanting machine is evaluated. Traditional systems use load cells to measure the weightof the tank however
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Rosales, Elisa Renee. "Predicting Patient Satisfaction With Ensemble Methods." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/595.

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Health plans are constantly seeking ways to assess and improve the quality of patient experience in various ambulatory and institutional settings. Standardized surveys are a common tool used to gather data about patient experience, and a useful measurement taken from these surveys is known as the Net Promoter Score (NPS). This score represents the extent to which a patient would, or would not, recommend his or her physician on a scale from 0 to 10, where 0 corresponds to "Extremely unlikely" and 10 to "Extremely likely". A large national health plan utilized automated calls to distribute such
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Feng, Wei. "Investigation of training data issues in ensemble classification based on margin concept : application to land cover mapping." Thesis, Bordeaux 3, 2017. http://www.theses.fr/2017BOR30016/document.

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La classification a été largement étudiée en apprentissage automatique. Les méthodes d’ensemble, qui construisent un modèle de classification en intégrant des composants d’apprentissage multiples, atteignent des performances plus élevées que celles d’un classifieur individuel. La précision de classification d’un ensemble est directement influencée par la qualité des données d’apprentissage utilisées. Cependant, les données du monde réel sont souvent affectées par les problèmes de bruit d’étiquetage et de déséquilibre des données. La marge d'ensemble est un concept clé en apprentissage d'ensemb
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Books on the topic "Bagging Forest"

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Vidales, A. Machine Learning with Matlab. Supervised Learning: Knn Classifiers, Ensemble Learning, Random Forest, Boosting and Bagging. Independently Published, 2019.

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López, César Pérez. DATA MINING and MACHINE LEARNING. PREDICTIVE TECHNIQUES : ENSEMBLE METHODS, BOOSTING, BAGGING, RANDOM FOREST, DECISION TREES and REGRESSION TREES.: Examples with MATLAB. Lulu Press, Inc., 2021.

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Book chapters on the topic "Bagging Forest"

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Mishra, Reyansh, Lakshay Gupta, Nitesh Gurbani, and Shiv Naresh Shivhare. "Image-Based Forest Fire Detection Using Bagging of Color Models." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3071-2_38.

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Denuit, Michel, Donatien Hainaut, and Julien Trufin. "Bagging Trees and Random Forests." In Springer Actuarial. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57556-4_4.

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Bui, Xuan-Nam, Hoang Nguyen, and Phonepaserth Soukhanouvong. "Extra Trees Ensemble: A Machine Learning Model for Predicting Blast-Induced Ground Vibration Based on the Bagging and Sibling of Random Forest Algorithm." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9770-8_43.

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Lombaert, Herve, Darko Zikic, Antonio Criminisi, and Nicholas Ayache. "Laplacian Forests: Semantic Image Segmentation by Guided Bagging." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10470-6_62.

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Richter, Stefan. "Regressions- und Klassifikationsbäume; Bagging, Boosting und Random Forests." In Statistisches und maschinelles Lernen. Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-59354-7_6.

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Zhao, He, Xiaojun Chen, Tung Nguyen, Joshua Zhexue Huang, Graham Williams, and Hui Chen. "Stratified Over-Sampling Bagging Method for Random Forests on Imbalanced Data." In Intelligence and Security Informatics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31863-9_5.

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Syam, Niladri, and Rajeeve Kaul. "Random Forest, Bagging, and Boosting of Decision Trees." In Machine Learning and Artificial Intelligence in Marketing and Sales. Emerald Publishing Limited, 2021. http://dx.doi.org/10.1108/978-1-80043-880-420211006.

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Wang Qing, Zhang Liang, Chi Mingmin, and Guo Jiankui. "MTForest: Ensemble Decision Trees based on Multi-Task Learning." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2008. https://doi.org/10.3233/978-1-58603-891-5-122.

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Many ensemble methods, such as Bagging, Boosting, Random Forest, etc, have been proposed and widely used in real world applications. Some of them are better than others on noise-free data while some of them are better than others on noisy data. But in reality, ensemble methods that can consistently gain good performance in situations with or without noise are more desirable. In this paper, we propose a new method namely MTForest, to ensemble decision tree learning algorihms by enumerating each input attribute as extra task to introduce different additional inductive bias to generate diverse ye
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Wijeratne, Ashansa Kithmini, Nirubikaa Ravikumar, Pulasthi Mithila Bandara, and Banujan Kuhaneswaran. "Prognostication of Crime Using Bagging Regression Model." In Advances in Library and Information Science. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4755-0.ch023.

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Crime is a social and economic problem that affects a country's quality of day-to-day life and economic growth. However, analyzing and forecasting crime is not a straightforward job for a law enforcement investigator to manually unravel the underlying nuances of crime data. To make this process easier and more automated, the authors present a machine-learning model for crime analysis and predictions. The authors used a London crime dataset and enhanced the data set by incorporating population density, percentage of economically inactive working age, and average monthly temperature. The pre-pro
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Lasota Tadeusz, Telec Zbigniew, Trawiński Bogdan, and Trawiński Grzegorz. "Evaluation of Random Subspace and Random Forest Regression Models Based on Genetic Fuzzy Systems." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-105-2-88.

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The random subspace and random forest ensemble methods using a genetic fuzzy rule-based system as a base learning algorithm were developed in Matlab environment. The methods were applied to the real-world regression problem of predicting the prices of residential premises based on historical data of sales/purchase transactions. The computationally intensive experiments were conducted aimed to compare the accuracy of ensembles generated by the proposed methods with bagging, repeated holdout, and repeated cross-validation models. The statistical analysis of results was made employing nonparametr
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Conference papers on the topic "Bagging Forest"

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Yiwei, Fu, and Shi Jingzhuo. "Course Learning Early Warning Model Based on Optimized Bagging-Stacking and Random Forest." In 2024 IEEE 2nd International Conference on Sensors, Electronics and Computer Engineering (ICSECE). IEEE, 2024. http://dx.doi.org/10.1109/icsece61636.2024.10729400.

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Primantara, Ari, Udisubakti Ciptomulyono, and Berlian Al Kindhi. "Bagging System Performance Analysis Using Artificial Neural Network, Random Forest Regression, Linear Regression, and Support Vector Regression." In 2024 IEEE International Symposium on Consumer Technology (ISCT). IEEE, 2024. https://doi.org/10.1109/isct62336.2024.10791247.

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Bhardwaj, Anay, Radha D., and V. S. Kirthika Devi. "An Enhanced Framework for Churn Prediction using Stratifed Bagging and Stacked Multi-Output Random Forests." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10914868.

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Ho, Yu Ting, Chun-Feng Wu, Ming-Chang Yang, Tseng-Yi Chen, and Yuan-Hao Chang. "Replanting Your Forest: NVM-friendly Bagging Strategy for Random Forest." In 2019 IEEE Non-Volatile Memory Systems and Applications Symposium (NVMSA). IEEE, 2019. http://dx.doi.org/10.1109/nvmsa.2019.8863525.

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Arfiani, A., and Z. Rustam. "Ovarian cancer data classification using bagging and random forest." In PROCEEDINGS OF THE 4TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES (ISCPMS2018). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5132473.

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Rosadi, Dedi, Widyastuti Andriyani, and Deasy Arisanty. "Prediction of forest fire using hybrid fuzzy-clustering - Bagging method." In INTERNATIONAL CONFERENCE ON APPLIED COMPUTATIONAL INTELLIGENCE AND ANALYTICS (ACIA-2022). AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0126618.

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Nagaraju, M. elam, Motamarri Vasavi Amrutha, Ramkuri Harika, Varisa Manikanta, and Pinnamaneni Sai Datta. "An Early Prediction of Cardiovascular Disease using Random Forest Bagging Method." In 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, 2023. http://dx.doi.org/10.1109/icaaic56838.2023.10141319.

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Al-Mousa, Amjed, Badr Mansour, Hamsa Al-Dabbagh, and Mohammad Radi. "Diagnosis of Polycystic Ovary Syndrome Using Random Forest with Bagging Technique." In 2023 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). IEEE, 2023. http://dx.doi.org/10.1109/jeeit58638.2023.10185873.

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Sapra, Varun, Luxmi Sapra, Ankit Vishnoi, Preeti Narooka, and Tanupriya Choudhury. "Enhancing Mental Disorder Diagnosis with Ensemble Bagging and Random Forest Techniques." In 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE). IEEE, 2024. http://dx.doi.org/10.1109/ic3se62002.2024.10593187.

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Sanjaya, Rangga, Fitriyani, Suharyanto, and Diah Puspitasari. "Noise Reduction through Bagging on Neural Network Algorithm for Forest Fire Estimates." In 2018 6th International Conference on Cyber and IT Service Management (CITSM). IEEE, 2018. http://dx.doi.org/10.1109/citsm.2018.8674287.

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