Literatura científica selecionada sobre o tema "Pruning random forest"
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Artigos de revistas sobre o assunto "Pruning random forest"
Yang, Fan, Wei-hang Lu, Lin-kai Luo e Tao Li. "Margin optimization based pruning for random forest". Neurocomputing 94 (outubro de 2012): 54–63. http://dx.doi.org/10.1016/j.neucom.2012.04.007.
Texto completo da fonteTarchoune, Ilhem, Akila Djebbar e Hayet Farida Merouani. "Improving Random Forest with Pre-pruning technique for Binary classification". All Sciences Abstracts 1, n.º 2 (25 de julho de 2023): 11. http://dx.doi.org/10.59287/as-abstracts.1202.
Texto completo da fonteFawagreh, Khaled, e Mohamed Medhat Gaber. "eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning". Big Data and Cognitive Computing 4, n.º 4 (28 de novembro de 2020): 37. http://dx.doi.org/10.3390/bdcc4040037.
Texto completo da fonteEl Habib Daho, Mostafa, Nesma Settouti, Mohammed El Amine Bechar, Amina Boublenza e Mohammed Amine Chikh. "A new correlation-based approach for ensemble selection in random forests". International Journal of Intelligent Computing and Cybernetics 14, n.º 2 (23 de março de 2021): 251–68. http://dx.doi.org/10.1108/ijicc-10-2020-0147.
Texto completo da fonteGefeller, Olaf, Asma Gul, Folkert Horn, Zardad Khan, Berthold Lausen e Werner Adler. "Ensemble Pruning for Glaucoma Detection in an Unbalanced Data Set". Methods of Information in Medicine 55, n.º 06 (2016): 557–63. http://dx.doi.org/10.3414/me16-01-0055.
Texto completo da fonteZhu, Wancai, Zhaogang Liu, Weiwei Jia e Dandan Li. "Modelling the Tree Height, Crown Base Height, and Effective Crown Height of Pinus koraiensis Plantations Based on Knot Analysis". Forests 12, n.º 12 (15 de dezembro de 2021): 1778. http://dx.doi.org/10.3390/f12121778.
Texto completo da fontePaudel, Nawaraj, e Jagdish Bhatta. "Mushroom Classification using Random Forest and REP Tree Classifiers". Nepal Journal of Mathematical Sciences 3, n.º 1 (31 de agosto de 2022): 111–16. http://dx.doi.org/10.3126/njmathsci.v3i1.44130.
Texto completo da fonteYadav, Dhyan Chandra, e Saurabh Pal. "Analysis of Heart Disease Using Parallel and Sequential Ensemble Methods With Feature Selection Techniques". International Journal of Big Data and Analytics in Healthcare 6, n.º 1 (janeiro de 2021): 40–56. http://dx.doi.org/10.4018/ijbdah.20210101.oa4.
Texto completo da fonteGonzález, Sergio, Francisco Herrera e Salvador García. "Monotonic Random Forest with an Ensemble Pruning Mechanism based on the Degree of Monotonicity". New Generation Computing 33, n.º 4 (julho de 2015): 367–88. http://dx.doi.org/10.1007/s00354-015-0402-4.
Texto completo da fonteMulyo, Harminto, e Nadia Annisa Maori. "PENINGKATAN AKURASI PREDIKSI PEMILIHAN PROGRAM STUDI CALON MAHASISWA BARU MELALUI OPTIMASI ALGORITMA DECISION TREE DENGAN TEKNIK PRUNING DAN ENSEMBLE". Jurnal Disprotek 15, n.º 1 (2 de janeiro de 2024): 15–25. http://dx.doi.org/10.34001/jdpt.v15i1.5585.
Texto completo da fonteTeses / dissertações sobre o assunto "Pruning random forest"
Diyar, Jamal. "Post-Pruning of Random Forests". Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15904.
Texto completo da fonteSammanfattning Kontext. Ensemble metoder fortsätter att få mer uppmärksamhet inom maskininlärning. Då maskininlärningstekniker som genererar en enskild klassificerare eller prediktor har visat tecken på begränsad kapacitet i vissa sammanhang, har ensemble metoder vuxit fram som alternativa metoder för att åstadkomma bättre prediktiva prestanda. En av de mest intressanta och effektiva ensemble algoritmerna som har introducerats under de senaste åren är Random Forests. För att säkerställa att Random Forests uppnår en hög prediktiv noggrannhet behöver oftast ett stort antal träd användas. Resultatet av att använda ett större antal träd för att öka den prediktiva noggrannheten är en komplex modell som kan vara svår att tolka eller analysera. Problemet med det stora antalet träd ställer dessutom högre krav på såväl lagringsutrymmet som datorkraften. Syfte. Denna uppsats utforskar möjligheten att automatiskt förenkla modeller som är genererade av Random Forests i syfte att reducera storleken på modellen, öka dess tolkningsbarhet, samt bevara eller förbättra den prediktiva noggrannheten. Syftet med denna uppsats är tvåfaldigt. Vi kommer först att jämföra och empiriskt utvärdera olika beskärningstekniker. Den andra delen av uppsatsen undersöker sambandet mellan den prediktiva noggrannheten och modellens tolkningsbarhet. Metod. Den primära forskningsmetoden som har använts för att genomföra den studien är experiment. Alla beskärningstekniker är implementerade i Python. För att träna, utvärdera, samt validera de olika modellerna, har fem olika datamängder använts. Resultat. Det finns inte någon signifikant skillnad i det prediktiva prestanda mellan de jämförda teknikerna och ingen av de undersökta beskärningsteknikerna är överlägsen på alla plan. Resultat från experimenten har också visat att sambandet mellan tolkningsbarhet och noggrannhet är proportionellt, i alla fall för de studerade konfigurationerna. Det vill säga, en positiv förändring i modellens tolkningsbarhet åtföljs av en negativ förändring i modellens noggrannhet. Slutsats. Det är möjligt att reducera storleken på en komplex Random Forests modell samt bibehålla eller förbättra den prediktiva noggrannheten. Dessutom beror valet av beskärningstekniken på användningsområdet och mängden träningsdata tillgänglig. Slutligen kan modeller som är signifikant förenklade vara mindre noggranna men å andra sidan tenderar de att uppfattas som mer förståeliga.
Fawagreh, Khaled. "On pruning and feature engineering in Random Forests". Thesis, Robert Gordon University, 2016. http://hdl.handle.net/10059/2113.
Texto completo da fonteCherfaoui, Farah. "Echantillonnage pour l'accélération des méthodes à noyaux et sélection gloutonne pour les représentations parcimonieuses". Electronic Thesis or Diss., Aix-Marseille, 2022. http://www.theses.fr/2022AIXM0256.
Texto completo da fonteThe contributions of this thesis are divided into two parts. The first part is dedicated to the acceleration of kernel methods and the second to optimization under sparsity constraints. Kernel methods are widely known and used in machine learning. However, the complexity of their implementation is high and they become unusable when the number of data is large. We first propose an approximation of Ridge leverage scores. We then use these scores to define a probability distribution for the sampling process of the Nyström method in order to speed up the kernel methods. We then propose a new kernel-based framework for representing and comparing discrete probability distributions. We then exploit the link between our framework and the maximum mean discrepancy to propose an accurate and fast approximation of the latter. The second part of this thesis is devoted to optimization with sparsity constraint for signal optimization and random forest pruning. First, we prove under certain conditions on the coherence of the dictionary, the reconstruction and convergence properties of the Frank-Wolfe algorithm. Then, we use the OMP algorithm to reduce the size of random forests and thus reduce the size needed for its storage. The pruned forest consists of a subset of trees from the initial forest selected and weighted by OMP in order to minimize its empirical prediction error
Capítulos de livros sobre o assunto "Pruning random forest"
Dheenadayalan, Kumar, G. Srinivasaraghavan e V. N. Muralidhara. "Pruning a Random Forest by Learning a Learning Algorithm". In Machine Learning and Data Mining in Pattern Recognition, 516–29. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41920-6_41.
Texto completo da fonteLi, Zhaozhao, Lide Wang, Ping Shen, Hui Song e Xiaomin Du. "Fault Diagnosis of MVB Based on Random Forest and Ensemble Pruning". In Lecture Notes in Electrical Engineering, 91–100. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2866-8_9.
Texto completo da fonteTaleb Zouggar, Souad, e Abdelkader Adla. "Measures of Random Forest Pruning: Comparative Study and Experiment on Diabetic Monitoring". In Advances in Intelligent Systems and Computing, 263–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36664-3_30.
Texto completo da fonteKiran, B. Ravi, e Jean Serra. "Cost-Complexity Pruning of Random Forests". In Lecture Notes in Computer Science, 222–32. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57240-6_18.
Texto completo da fonteFawagreh, Khaled, Mohamed Medhat Gaber e Eyad Elyan. "CLUB-DRF: A Clustering Approach to Extreme Pruning of Random Forests". In Research and Development in Intelligent Systems XXXII, 59–73. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25032-8_4.
Texto completo da fonteFawagreh, Khaled, Mohamed Medhat Gaber e Eyad Elyan. "An Outlier Ranking Tree Selection Approach to Extreme Pruning of Random Forests". In Engineering Applications of Neural Networks, 267–82. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44188-7_20.
Texto completo da fonteAhmad, Mahmood, Xiaowei Tang e Feezan Ahmad. "Evaluation of Liquefaction-Induced Settlement Using Random Forest and REP Tree Models: Taking Pohang Earthquake as a Case of Illustration". In Natural Hazards - Impacts, Adjustments and Resilience. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.94274.
Texto completo da fonteHeutte, Laurent, Caroline Petitjean e Chesner Désir. "PRUNING TREES IN RANDOM FORESTS FOR MINIMIZING NON DETECTION IN MEDICAL IMAGING". In Handbook of Pattern Recognition and Computer Vision, 89–107. WORLD SCIENTIFIC, 2015. http://dx.doi.org/10.1142/9789814656535_0005.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Pruning random forest"
Rose, Minu, e Hani Ragab Hassen. "A Survey of Random Forest Pruning Techniques". In 9th International Conference on Computer Science, Engineering and Applications. Aircc publishing Corporation, 2019. http://dx.doi.org/10.5121/csit.2019.91808.
Texto completo da fonteKulkarni, Vrushali Y., e Pradeep K. Sinha. "Pruning of Random Forest classifiers: A survey and future directions". In 2012 International Conference on Data Science & Engineering (ICDSE). IEEE, 2012. http://dx.doi.org/10.1109/icdse.2012.6282329.
Texto completo da fonteLiu, Xin, Qifeng Zhou e Fan Yang. "Leaf node-level ensemble pruning approaches based on node-sample correlation for random forest". In IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2017. http://dx.doi.org/10.1109/iecon.2017.8217016.
Texto completo da fonteLiang, Yu-Pei, Yung-Han Hsu, Tseng-Yi Chen, Shuo-Han Chen, Hsin-Wen Wei, Tsan-sheng Hsu e Wei-Kuan Shih. "Eco-feller: Minimizing the Energy Consumption of Random Forest Algorithm by an Eco-pruning Strategy over MLC NVRAM". In 2021 58th ACM/IEEE Design Automation Conference (DAC). IEEE, 2021. http://dx.doi.org/10.1109/dac18074.2021.9586164.
Texto completo da fonteAl-Khudafi, Abbas M., Hamzah A. Al-Sharifi, Ghareb M. Hamada, Mohamed A. Bamaga, Abdulrahman A. Kadi e A. A. Al-Gathe. "Evaluation of Different Tree-Based Machine Learning Approaches for Formation Lithology Classification". In International Geomechanics Symposium. ARMA, 2023. http://dx.doi.org/10.56952/igs-2023-0026.
Texto completo da fonteAl-Sharifi, H. A., A. M. Alkhudafi, A. A. Al-Gathe, S. O. Baarimah, Wahbi Al-Ameri e A. T. Alyazidi. "Prediction of Two-Phase Flow Regimes in Vertical Pipes Using Tree-Based Ensemble Models". In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-24084-ms.
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