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Academic literature on the topic 'Bagging Forest'

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

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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 (July 24, 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 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.
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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 (July 1, 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-known benchmark datasets and compared with both bagging, random forest and boosting techniques using well-known classification algorithms: Support Vector Machines (SVM), decision trees (C4.5), k-Nearest Neighbour (kNN) and Naive Bayes (NB). The results show that eBagging outperforms its counterparts by classifying the data points more accurately while reducing the training error
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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 (April 30, 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 statistical counterpart, logistic regression.FindingsThe results showed that random forests and bagging outperform other methods in terms of predictive power.Originality/valueThis is the first study to assess the impact of environmental factors on banking performance in Middle East and North Africa countries.
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Kotsiantis, Sotiris. "Combining bagging, boosting, rotation forest and random subspace methods." Artificial Intelligence Review 35, no. 3 (December 21, 2010): 223–40. http://dx.doi.org/10.1007/s10462-010-9192-8.

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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 forest. We propose two new resampling-based methods to resemble the original data and covariance structure: stochastic inverse-probability oversampling and parametric inverse-probability bagging. We compare all techniques for the random forest and other classifiers, both theoretically and on simulated and real data. Empirical results show that the random forest profits from only the parametric inverse-probability bagging proposed by us. For other classifiers, correction is mostly advantageous, and methods perform uniformly. We discuss consequences of inappropriate distribution assumptions and reason for different behaviors between the random forest and other classifiers. In conclusion, we provide guidance for choosing correction methods when training classifiers on biased samples. For random forests, our method outperforms state-of-the-art procedures if distribution assumptions are roughly fulfilled. We provide our implementation in the R packagesambia.
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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 (December 6, 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, Naïve Bayes classifier, Random Forest dan Bagging Classifier. Tujuan dari penelitian ini adalah untuk menyelesaikan semua masalah SMS yang teridentifikasi spam yang banyak terjadi pada saat ini sehingga dapat memberikan masukan dalam perbandingan metode yang mampu menyaring dan memisahkan sms spam dan sms non spam. Pada penelitian ini menghasilkan bahwa Bagging classifier algorithm ini mendapatkan ferformance score tertinggi dari algoritma yang lain yang dapat dipergunakan sebagai sarana untuk memfiltrasi SMS yang masuk ke dalam inbox pengguna dan Bagging classifier algorithm dapat memberikan hasil filtrasi yang akurat untuk menyaring SMS yang masuk.
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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 (May 2, 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 Bagging digunakan untuk menangani ketidakseimbangan kelas yang ada pada dataset ini dan algoritma Naïve Bayes sebagai algoritma machine learning yang diimplementasikan dalam penelitian ini. Hasil akurasi terbaik adalah sebesar 98.40% pada model Naive Bayes, Bagging dan Greedy Forward Selection dan 92.63% pada model Naïve Bayes dan Bagging.
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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 and even than other previous credal tree models. In this paper, the performance of the CC4.5 model in bagging schemes on noisy domains is shown. An experimental study on data sets with added noise is carried out in order to compare results where bagging schemes are applied on credal trees and C4.5 procedure. As a benchmark point, the known Random Forest (RF) classification method is also used. It will be shown that the bagging ensemble using pruned credal trees outperforms the successful bagging C4.5 and RF when data sets with medium-to-high noise level are classified.
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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 (March 19, 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 essential to address this. To solve this issue, much research has studied prediction models, and machine learning is one of the typical methods. In this paper, we used a bagging model to predict solar energy output. Bagging generally uses a decision tree as a base learner. However, to improve forecasting accuracy, we proposed a bagging model using an ensemble model as a base learner and adding past output data as new features. We set base learners as ensemble models, such as random forest, XGBoost, and LightGBMs. Also, we used past output data as new features. Results showed that the ensemble learner-based bagging model using past data features performed more accurately than the bagging model using a single model learner with default features.
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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 (February 28, 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. Optimization of the RF algorithm can be done using Bagging and Genetic Algorithm (GA). This study aims to classify Bank Marketing data in the form of loan application receipts, which data is taken from the www.data.world site. Classification is carried out using the RF algorithm to obtain a predictive model for loan application acceptance with optimal accuracy. This study will also compare the use of optimization in the RF algorithm with Bagging and Genetic Algorithms. Based on the tests that have been done, the results show that the most optimal performance of the classification of Bank Marketing data is by using the RF algorithm with an accuracy of 88.30%, AUC (+) of 0.500 and AUC (-) of 0.000. The optimization of Bagging and Genetic Algorithm has not been able to improve the performance of the RF algorithm for classification of Bank Marketing data.
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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 He, Ar, N, O, y Hg, en sus niveles de energía uno y dos, mediante técnicas de Machine Learning (ML). En primer lugar, se descargan las líneas de elementos reportadas en el NIST (National Institute of Standards and Technology), después se preprocesan y unifican para los siguientes procesos: a) crear un generador de 84 espectros sintéticos implementado en Python y el módulo ipywidgets de Jupyter Notebook, con las posibilidades de elegir un elemento, nivel de energía, variar la temperatura, anchura a media altura, y normalizar el especto y, b) extraer las líneas para los elementos He, Ar, N, O y Hg en el rango de los 200 nm a 890 nm, posteriormente, se les aplica sobremuestreo para realizar la búsqueda de hiperparámetros para los algoritmos: Decision Tree, Bagging, Random Forest y Extremely Randomized Trees basándose en los principios del diseño de experimentos de aleatorización, replicación, bloqueo y estratificación.
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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 % rätt och rensningar och skott på mål var de viktigaste prestationsvariablerna. Sedan undersöktes vilka prediktionsvariabler som var bäst. Den bästa modellen klassificerade rätt slutresultat cirka 88 % av matcherna. Utifrån vad författarna ansågs vara de viktigaste prediktionsvariablerna skapades en prediktionsmodell med färre variabler. Denna lyckades klassificera rätt cirka 86 % av matcherna. Prediktionsmodellen var konstruerad med spelarbetyg, odds på oavgjort och domare.
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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 a survey to its members and was interested in understanding what factors contributed to a patient's satisfaction. Additionally, they were interested in whether or not NPS could be predicted using responses from other questions on the survey, along with demographic data. When the distribution of various predictors was compared between the less satisfied and highly satisfied members, there was significant overlap, indicating that not even the Bayes Classifier could successfully differentiate between these members. Moreover, the highly imbalanced proportion of NPS responses resulted in initial poor prediction accuracy. Thus, due to the non-linear structure of the data, and high number of categorical predictors, we have leveraged flexible methods, such as decision trees, bagging, and random forests, for modeling and prediction. We further altered the prediction step in the random forest algorithm in order to account for the imbalanced structure of the data.
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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 calculating the sound pressure level in dB. We achieve a measurement error of less than 1 dB. Our machine learning-based method for noise classification uses Mel-frequency cepstral coefficients for audio feature extraction and four supervised classification algorithms (that is, support vector machine, k-nearest neighbors, bootstrap aggregating, and random forest). We evaluate our approach experimentally with a dataset of about 3000 sound samples grouped in eight sound classes (such as car horn, jackhammer, or street music). We explore the parameter space of the four algorithms to estimate the optimal parameter values for the classification of sound samples in the dataset under study. We achieve noise classification accuracy in the range of 88% – 94%.
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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, these types of systems are expensive to build and cumbersome torepair. They also add a lot of weight to the equipment which increases the fuel consump-tion of the tractor. Thus, this thesis investigates the use of radar sensors together witha number of Machine Learning algorithms. Fourteen radar sensors are fitted to a tankat different positions, data is collected, and a preprocessing method is developed. Then,the data is used to test the following Machine Learning algorithms: Bagged RegressionTrees (BG), Random Forest Regression (RF), Boosted Regression Trees (BRT), LinearRegression (LR), Linear Support Vector Machine (L-SVM), Multi-Layer Perceptron Re-gressor (MLPR). The model with the best 5-fold crossvalidation scores was Random For-est, closely followed by Boosted Regression Trees. A robustness test, using 5 previouslyunseen scenarios, revealed that the Boosted Regression Trees model was the most robust.The radar position analysis showed that 6 sensors together with the MLPR model gavethe best RMSE scores.In conclusion, the models performed well on this type of system which shows thatthey might be a competitive alternative to load cell based systems.
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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'ensemble. Elle a été utilisée aussi bien pour l'analyse théorique que pour la conception d'algorithmes d'apprentissage automatique. De nombreuses études ont montré que la performance de généralisation d'un classifieur ensembliste est liée à la distribution des marges de ses exemples d'apprentissage. Ce travail se focalise sur l'exploitation du concept de marge pour améliorer la qualité de l'échantillon d'apprentissage et ainsi augmenter la précision de classification de classifieurs sensibles au bruit, et pour concevoir des ensembles de classifieurs efficaces capables de gérer des données déséquilibrées. Une nouvelle définition de la marge d'ensemble est proposée. C'est une version non supervisée d'une marge d'ensemble populaire. En effet, elle ne requière pas d'étiquettes de classe. Les données d'apprentissage mal étiquetées sont un défi majeur pour la construction d'un classifieur robuste que ce soit un ensemble ou pas. Pour gérer le problème d'étiquetage, une méthode d'identification et d'élimination du bruit d'étiquetage utilisant la marge d'ensemble est proposée. Elle est basée sur un algorithme existant d'ordonnancement d'instances erronées selon un critère de marge. Cette méthode peut atteindre un taux élevé de détection des données mal étiquetées tout en maintenant un taux de fausses détections aussi bas que possible. Elle s'appuie sur les valeurs de marge des données mal classifiées, considérant quatre différentes marges d'ensemble, incluant la nouvelle marge proposée. Elle est étendue à la gestion de la correction du bruit d'étiquetage qui est un problème plus complexe. Les instances de faible marge sont plus importantes que les instances de forte marge pour la construction d'un classifieur fiable. Un nouvel algorithme, basé sur une fonction d'évaluation de l'importance des données, qui s'appuie encore sur la marge d'ensemble, est proposé pour traiter le problème de déséquilibre des données. Cette méthode est évaluée, en utilisant encore une fois quatre différentes marges d'ensemble, vis à vis de sa capacité à traiter le problème de déséquilibre des données, en particulier dans un contexte multi-classes. En télédétection, les erreurs d'étiquetage sont inévitables car les données d'apprentissage sont typiquement issues de mesures de terrain. Le déséquilibre des données d'apprentissage est un autre problème fréquent en télédétection. Les deux méthodes d'ensemble proposées, intégrant la définition de marge la plus pertinente face à chacun de ces deux problèmes majeurs affectant les données d'apprentissage, sont appliquées à la cartographie d'occupation du sol
Classification has been widely studied in machine learning. Ensemble methods, which build a classification model by integrating multiple component learners, achieve higher performances than a single classifier. The classification accuracy of an ensemble is directly influenced by the quality of the training data used. However, real-world data often suffers from class noise and class imbalance problems. Ensemble margin is a key concept in ensemble learning. It has been applied to both the theoretical analysis and the design of machine learning algorithms. Several studies have shown that the generalization performance of an ensemble classifier is related to the distribution of its margins on the training examples. This work focuses on exploiting the margin concept to improve the quality of the training set and therefore to increase the classification accuracy of noise sensitive classifiers, and to design effective ensemble classifiers that can handle imbalanced datasets. A novel ensemble margin definition is proposed. It is an unsupervised version of a popular ensemble margin. Indeed, it does not involve the class labels. Mislabeled training data is a challenge to face in order to build a robust classifier whether it is an ensemble or not. To handle the mislabeling problem, we propose an ensemble margin-based class noise identification and elimination method based on an existing margin-based class noise ordering. This method can achieve a high mislabeled instance detection rate while keeping the false detection rate as low as possible. It relies on the margin values of misclassified data, considering four different ensemble margins, including the novel proposed margin. This method is extended to tackle the class noise correction which is a more challenging issue. The instances with low margins are more important than safe samples, which have high margins, for building a reliable classifier. A novel bagging algorithm based on a data importance evaluation function relying again on the ensemble margin is proposed to deal with the class imbalance problem. In our algorithm, the emphasis is placed on the lowest margin samples. This method is evaluated using again four different ensemble margins in addressing the imbalance problem especially on multi-class imbalanced data. In remote sensing, where training data are typically ground-based, mislabeled training data is inevitable. Imbalanced training data is another problem frequently encountered in remote sensing. Both proposed ensemble methods involving the best margin definition for handling these two major training data issues are applied to the mapping of land covers
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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, 477–86. Singapore: 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, 107–30. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57556-4_4.

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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, 496–504. Cham: 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, 163–220. Berlin, Heidelberg: 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, 63–72. Cham: 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, 139–82. Emerald Publishing Limited, 2021. http://dx.doi.org/10.1108/978-1-80043-880-420211006.

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Settouti, Nesma, Mostafa El Habib Daho, Mohammed El Amine Bechar, and Mohammed Amine Chikh. "An Optimized Semi-Supervised Learning Approach for High Dimensional Datasets." In Advances in Bioinformatics and Biomedical Engineering, 294–321. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2607-0.ch012.

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The semi-supervised learning is one of the most interesting fields for research developments in the machine learning domain beyond the scope of supervised learning from data. Medical diagnostic process works mostly in supervised mode, but in reality, we are in the presence of a large amount of unlabeled samples and a small set of labeled examples characterized by thousands of features. This problem is known under the term “the curse of dimensionality”. In this study, we propose, as solution, a new approach in semi-supervised learning that we would call Optim Co-forest. The Optim Co-forest algorithm combines the re-sampling data approach (Bagging Breiman, 1996) with two selection strategies. The first one involves selecting random subset of parameters to construct the ensemble of classifiers following the principle of Co-forest (Li & Zhou, 2007). The second strategy is an extension of the importance measure of Random Forest (RF; Breiman, 2001). Experiments on high dimensional datasets confirm the power of the adopted selection strategies in the scalability of our method.
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Chinnaswamy, Arunkumar, and Ramakrishnan Srinivasan. "Performance Analysis of Classifiers on Filter-Based Feature Selection Approaches on Microarray Data." In Bio-Inspired Computing for Information Retrieval Applications, 41–70. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2375-8.ch002.

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The process of Feature selection in machine learning involves the reduction in the number of features (genes) and similar activities that results in an acceptable level of classification accuracy. This paper discusses the filter based feature selection methods such as Information Gain and Correlation coefficient. After the process of feature selection is performed, the selected genes are subjected to five classification problems such as Naïve Bayes, Bagging, Random Forest, J48 and Decision Stump. The same experiment is performed on the raw data as well. Experimental results show that the filter based approaches reduce the number of gene expression levels effectively and thereby has a reduced feature subset that produces higher classification accuracy compared to the same experiment performed on the raw data. Also Correlation Based Feature Selection uses very fewer genes and produces higher accuracy compared to Information Gain based Feature Selection approach.
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Verma, B. "Neural Network Based Classifier Ensembles." In Machine Learning Algorithms for Problem Solving in Computational Applications, 229–39. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1833-6.ch014.

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This chapter presents the state of the art in classifier ensembles and their comparative performance analysis. The main aim and focus of this chapter is to present and compare the author’s recently developed neural network based classifier ensembles. The three types of neural classifier ensembles are considered and discussed. The first type is a classifier ensemble that uses a neural network for all its base classifiers. The second type is a classifier ensemble that uses a neural network as one of the classifiers among many of its base classifiers. The third and final type is a classifier ensemble that uses a neural network as a fusion classifier. The chapter reviews recent neural network based ensemble classifiers and compares their performances with other machine learning based classifier ensembles such as bagging, boosting, and rotation forest. The comparison is conducted on selected benchmark datasets from UCI machine learning repository.
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Dash, Sujata. "Hybrid Ensemble Learning Methods for Classification of Microarray Data." In Handbook of Research on Computational Intelligence Applications in Bioinformatics, 17–36. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0427-6.ch002.

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Efficient classification and feature extraction techniques pave an effective way for diagnosing cancers from microarray datasets. It has been observed that the conventional classification techniques have major limitations in discriminating the genes accurately. However, such kind of problems can be addressed by an ensemble technique to a great extent. In this paper, a hybrid RotBagg ensemble framework has been proposed to address the problem specified above. This technique is an integration of Rotation Forest and Bagging ensemble which in turn preserves the basic characteristics of ensemble architecture i.e., diversity and accuracy. Three different feature selection techniques are employed to select subsets of genes to improve the effectiveness and generalization of the RotBagg ensemble. The efficiency is validated through five microarray datasets and also compared with the results of base learners. The experimental results show that the correlation based FRFR with PCA-based RotBagg ensemble form a highly efficient classification model.
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Conference papers on the topic "Bagging Forest"

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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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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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Stepanov, Nikolai, Daria Alekseeva, Aleksandr Ometov, and Elena Simona Lohan. "Applying Machine Learning to LTE Traffic Prediction: Comparison of Bagging, Random Forest, and SVM." In 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). IEEE, 2020. http://dx.doi.org/10.1109/icumt51630.2020.9222418.

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Iriawan, Nur, Kartika Fithriasari, Brodjol Sutijo Suprih Ulama, Wahyuni Suryaningtyas, Sinta Septi Pangastuti, Nita Cahyani, and Laila Qadrini. "On The Comparison: Random Forest, SMOTE-Bagging, and Bernoulli Mixture to Classify Bidikmisi Dataset in East Java." In 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM). IEEE, 2018. http://dx.doi.org/10.1109/cenim.2018.8711035.

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Santos, Gabriel, Felipe Dos Santos, Aline Rocha, and Thiago Da Silva. "Utilização de aprendizagem de máquina para a identificação de dependência em aparelhos celulares com foco em casos que possam causar reprovação e evasão." In Escola Regional de Computação Ceará, Maranhão, Piauí. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/ercemapi.2020.11489.

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Este trabalho introduzir os problemas causados pelo uso excessivo dos celulares na sociedade moderna, e mostrar que eles podem causar problemas escolares aos alunos que o usa de forma demasiada. Para isto fizemos uma integração da tecnologia com o uso de uma área da inteligência artificial, o aprendizado de máquina, com dados de uma base de dados específica. Para a tarefa proposta foram usados os classificadores Naive Bayes, AdaBoost, SVM, Bagging e Random Forest, ao final dos testes o classificador SVM apresentou o melhor desempenho no contexto geral.
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"Ensemble Learning Approach for Clickbait Detection Using Article Headline Features." In InSITE 2019: Informing Science + IT Education Conferences: Jerusalem. Informing Science Institute, 2019. http://dx.doi.org/10.28945/4319.

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[This Proceedings paper was revised and published in the 2019 issue of the journal Informing Science: The International Journal of an Emerging Transdiscipline, Volume 22] Aim/Purpose: The aim of this paper is to propose an ensemble learners based classification model for classification clickbaits from genuine article headlines. Background: Clickbaits are online articles with deliberately designed misleading titles for luring more and more readers to open the intended web page. Clickbaits are used to tempted visitors to click on a particular link either to monetize the landing page or to spread the false news for sensationalization. The presence of clickbaits on any news aggregator portal may lead to an unpleasant experience for readers. Therefore, it is essential to distinguish clickbaits from authentic headlines to mitigate their impact on readers’ perception. Methodology: A total of one hundred thousand article headlines are collected from news aggregator sites consists of clickbaits and authentic news headlines. The collected data samples are divided into five training sets of balanced and unbalanced data. The natural language processing techniques are used to extract 19 manually selected features from article headlines. Contribution: Three ensemble learning techniques including bagging, boosting, and random forests are used to design a classifier model for classifying a given headline into the clickbait or non-clickbait. The performances of learners are evaluated using accuracy, precision, recall, and F-measures. Findings: It is observed that the random forest classifier detects clickbaits better than the other classifiers with an accuracy of 91.16 %, a total precision, recall, and f-measure of 91 %.
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Abbas, Mohammed A., and Watheq J. Al-Mudhafar. "Lithofacies Classification of Carbonate Reservoirs Using Advanced Machine Learning: A Case Study from a Southern Iraqi Oil Field." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/31114-ms.

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Abstract Estimating rock facies from petrophysical logs in non-cored wells in complex carbonates represents a crucial task for improving reservoir characterization and field development. Thus, it most essential to identify the lithofacies that discriminate the reservoir intervals based on their flow and storage capacity. In this paper, an innovative procedure is adopted for lithofacies classification using data-driven machine learning in a well from the Mishrif carbonate reservoir in the giant Majnoon oil field, Southern Iraq. The Random Forest method was adopted for lithofacies classification using well logging data in a cored well to predict their distribution in other non-cored wells. Furthermore, three advanced statistical algorithms: Logistic Boosting Regression, Bagging Multivariate Adaptive Regression Spline, and Generalized Boosting Modeling were implemented and compared to the Random Forest approach to attain the most realistic lithofacies prediction. The dataset includes the measured discrete lithofacies distribution and the original log curves of caliper, gamma ray, neutron porosity, bulk density, sonic, deep and shallow resistivity, all available over the entire reservoir interval. Prior to applying the four classification algorithms, a random subsampling cross-validation was conducted on the dataset to produce training and testing subsets for modeling and prediction, respectively. After predicting the discrete lithofacies distribution, the Confusion Table and the Correct Classification Rate Index (CCI) were employed as further criteria to analyze and compare the effectiveness of the four classification algorithms. The results of this study revealed that Random Forest was more accurate in lithofacies classification than other techniques. It led to excellent matching between the observed and predicted discrete lithofacies through attaining 100% of CCI based on the training subset and 96.67 % of the CCI for the validating subset. Further validation of the resulting facies model was conducted by comparing each of the predicted discrete lithofacies with the available ranges of porosity and permeability obtained from the NMR log. We observed that rudist-dominated lithofacies correlates to rock with higher porosity and permeability. In contrast, the argillaceous lithofacies correlates to rocks with lower porosity and permeability. Additionally, these high-and low-ranges of permeability were later compared with the oil rate obtained from the PLT log data. It was identified that the high-and low-ranges of permeability correlate well to the high- and low-oil rate logs, respectively. In conclusion, the high quality estimation of lithofacies in non-cored intervals and wells is a crucial reservoir characterization task in order to obtain meaningful permeability-porosity relationships and capture realistic reservoir heterogeneity. The application of machine learning techniques drives down costs, provides for time-savings, and allows for uncertainty mitigation in lithofacies classification and prediction. The entire workflow was done through R, an open-source statistical computing language. It can easily be applied to other reservoirs to attain for them a similar improved overall reservoir characterization.
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Hegde, Chiranth, Scott Wallace, and Ken Gray. "Using Trees, Bagging, and Random Forests to Predict Rate of Penetration During Drilling." In SPE Middle East Intelligent Oil and Gas Conference and Exhibition. Society of Petroleum Engineers, 2015. http://dx.doi.org/10.2118/176792-ms.

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Chaeibakhsh, Sarvenaz, Elissa Phillips, Amanda Buchanan, and Eric Wade. "Upper extremity post-stroke motion quality estimation with decision trees and bagging forests." In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2016. http://dx.doi.org/10.1109/embc.2016.7591748.

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