Добірка наукової літератури з теми "Bagging Forest"
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Статті в журналах з теми "Bagging Forest"
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.
Повний текст джерелаTuysuzoglu, 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.
Повний текст джерелаAnouze, 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.
Повний текст джерелаKotsiantis, 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.
Повний текст джерелаKrautenbacher, 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.
Повний текст джерелаIrawan, 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.
Повний текст джерелаFitriyani, 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.
Повний текст джерелаAbellán, 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.
Повний текст джерелаChoi, 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.
Повний текст джерелаYoga 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.
Повний текст джерелаДисертації з теми "Bagging Forest"
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.
Повний текст джерелаБулах, 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.
Повний текст джерелаAssareh, Amin. "OPTIMIZING DECISION TREE ENSEMBLES FOR GENE-GENE INTERACTION DETECTION." Kent State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=kent1353971575.
Повний текст джерелаYang, 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.
Повний текст джерелаZoghi, 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.
Повний текст джерелаUlriksson, 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.
Повний текст джерелаRosales, Elisa Renee. "Predicting Patient Satisfaction With Ensemble Methods." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/595.
Повний текст джерелаAlsouda, 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.
Повний текст джерелаThorén, 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.
Повний текст джерелаFeng, 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.
Повний текст джерелаClassification 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
Частини книг з теми "Bagging Forest"
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.
Повний текст джерелаDenuit, 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.
Повний текст джерелаLombaert, 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.
Повний текст джерелаRichter, 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.
Повний текст джерелаZhao, 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.
Повний текст джерелаSyam, 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.
Повний текст джерелаSettouti, 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.
Повний текст джерелаChinnaswamy, 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.
Повний текст джерелаVerma, 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.
Повний текст джерелаDash, 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.
Повний текст джерелаТези доповідей конференцій з теми "Bagging Forest"
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.
Повний текст джерелаArfiani, 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.
Повний текст джерелаSanjaya, 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.
Повний текст джерелаStepanov, 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.
Повний текст джерелаIriawan, 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.
Повний текст джерелаSantos, 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.
Повний текст джерела"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.
Повний текст джерелаAbbas, 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.
Повний текст джерелаHegde, 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.
Повний текст джерелаChaeibakhsh, 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.
Повний текст джерела