Relevant bibliographies by topics / Classification tree

Contents

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

Academic literature on the topic 'Classification tree'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Classification tree"

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Govedar, Zoran. "Classification of trees and the effects of thinning in the artificially established spruce stand in the area of Sokolina-Kotor Varos." Bulletin of the Faculty of Forestry, no. 96 (2007): 29–43. http://dx.doi.org/10.2298/gsf0796029g.

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The classifications of trees are mainly based on descriptive (attributive) characters and they have a great significance in thinning. In forestry practice (tree marking for felling, forest inventory, etc) in the Republic of Srpska, the most frequently applied classifications are silvicultural-technical (UT) and technical classification of trees, which are based on the knowledge of tree species, stem diameter and stem quality. In IUFRO classification, based on the silvicultural role of trees, and in UT classification, the trees are classified in three categories. The knowledge and application o
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Fortin, Mathieu, François Guillemette, and Steve Bédard. "Predicting volumes by log grades in standing sugar maple and yellow birch trees in southern Quebec, Canada." Canadian Journal of Forest Research 39, no. 10 (2009): 1928–38. http://dx.doi.org/10.1139/x09-108.

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Modelling volumes by log grades in standing hardwood trees is often hindered by the nature of the response variable. In this paper, we used a two-part conditional model to account for the excess of zero responses for some log grades. Moreover, this approach was used as a framework to compare three different tree classifications in their ability to predict volumes by log grades in standing yellow birch ( Betula alleghaniensis Britton) and sugar maple ( Acer saccharum Marsh.) trees. A tree grade classification was compared with two preharvest tree classifications based on mortality risk assessme
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Povhan, Igor. "Method of algorithmic classification trees based on constraints." Bulletin of the National Technical University "KhPI" A series of "Information and Modeling", no. 1 (5) (October 25, 2021): 17–38. http://dx.doi.org/10.20998/2411-0558.2021.01.02.

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The general problem of constructing algorithmic recognition (classification) trees based on a limited method in the theory of artificial intelligence is considered. The object of this research is the concept of an algorithmic classification tree based on a bounded method. The subject of the research is actual methods, algorithms and schemes (limited method) for constructing algorithmic classification trees. A limited method for constructing algorithmic classification trees is proposed, which for a given initial training sample of any size builds a tree structure (algorithm tree model), which c
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Zhang, Hui. "The Analysis of English Sentence Components Based on Decision Tree Classification Algorithm." Highlights in Science, Engineering and Technology 23 (December 3, 2022): 317–20. http://dx.doi.org/10.54097/hset.v23i.3617.

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Decision tree is an important classification method in data excavation technology. It is a predictive analysis model expressed in the form of a tree structure (including binary trees and poly trees). The decision tree method is a more general classification function approximation method. It is an algorithm commonly used in predictive models to find some potentially valuable information by purposefully classifying a large amount of data. In this article, the author tries to analyze the English sentence components based on the decision tree classification algorithm. The author starts with the de
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Kwader, John. "Keen’s Tree Classification." Journal of Forestry 109, no. 2 (2011): 68. http://dx.doi.org/10.1093/jof/109.2.68c.

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Verbyla, David L. "Classification trees: a new discrimination tool." Canadian Journal of Forest Research 17, no. 9 (1987): 1150–52. http://dx.doi.org/10.1139/x87-177.

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Classification trees are discriminant models structured as dichtomous keys. A simple classification tree is presented and contrasted with a linear discriminant function. Classification trees have several advantages when compared with linear discriminant analysis. The method is robust with respect to outlier cases. It is nonparametric and can use nominal, ordinal, interval, and ratio scaled predictor variables. Cross-validation is used during tree development to prevent overrating the tree with too many predictor variables. Missing values are handled by using surrogate splits based on nonmissin
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Povkhan, I. F., O. V. Mitsa, O. Y. Mulesa, and V. V. Polishchuk. "THE ALGORITHM TREE METHOD IN SOLVING THE TASK OF CLASSIFYING HYDROGRAPHIC DATA." Radio Electronics, Computer Science, Control, no. 4 (January 11, 2022): 78–94. http://dx.doi.org/10.15588/1607-3274-2021-4-8.

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Context. The work is dedicated to the identification of a simple and effective mechanism by which it is possible to build algorithmic classification trees (algorithmic tree models) on the basis of fixed initial information in the form of a discrete data training sample. The constructed algorithmic classification tree will unmistakably classify (recognize) the entire training sample on which the model is built, have a minimum structure (structural complexity) and consist of components – autonomous classification and recognition algorithms as the vertices of the structure (attributes of the tree
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Igor, Povkhan. "Classification models of flood-related events based on algorithm trees." Eastern-European Journal of Enterprise Technologies 6, no. 4(108) (2020): 58–68. https://doi.org/10.15587/1729-4061.2020.219525.

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This paper reports the construction of an effective mechanism for synthesizing classification trees according to the fixed initial information in the form of a training sample for the task of recognizing the current state, as well as flood phenomena, of river basins. The built algorithmic classification tree could unmistakably categorize the entire training sample underlying the constructed classification scheme. Moreover, it would demonstrate minimal structural complexity by including components such as the algorithms for autonomous classification and recognition to serve the structure’
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Wang, Yutang, Jia Wang, Shuping Chang, et al. "Classification of Street Tree Species Using UAV Tilt Photogrammetry." Remote Sensing 13, no. 2 (2021): 216. http://dx.doi.org/10.3390/rs13020216.

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As an important component of the urban ecosystem, street trees have made an outstanding contribution to alleviating urban environmental pollution. Accurately extracting tree characteristics and species information can facilitate the monitoring and management of street trees, as well as aiding landscaping and studies of urban ecology. In this study, we selected the suburban areas of Beijing and Zhangjiakou and investigated six representative street tree species using unmanned aerial vehicle (UAV) tilt photogrammetry. We extracted five tree attributes and four combined attribute parameters and u
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Wang, Yutang, Jia Wang, Shuping Chang, et al. "Classification of Street Tree Species Using UAV Tilt Photogrammetry." Remote Sensing 13, no. 2 (2021): 216. http://dx.doi.org/10.3390/rs13020216.

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As an important component of the urban ecosystem, street trees have made an outstanding contribution to alleviating urban environmental pollution. Accurately extracting tree characteristics and species information can facilitate the monitoring and management of street trees, as well as aiding landscaping and studies of urban ecology. In this study, we selected the suburban areas of Beijing and Zhangjiakou and investigated six representative street tree species using unmanned aerial vehicle (UAV) tilt photogrammetry. We extracted five tree attributes and four combined attribute parameters and u
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Dissertations / Theses on the topic "Classification tree"

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Carlsson, David. "Tree trunk image classifier : Image classification of trees using Collaboratory, Keras and TensorFlow." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-98698.

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In the forestry industry tree trunks are currently classified manually. The object of this thesis is to answer whether it is possible to automate this using modern computer hardware and image-classification of tree-trunks using machine learning algorithms. The report concludes, based on results from controlled experiments that it is possible to achieve an accuracy above 90% across the genuses Birch, Pine and Spruce with a classification-time per tree shorter than 500 milli seconds. The report further compares these results against previous research and concludes that better results are probabl
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Johansson, Henrik. "Video Flow Classification : Feature Based Classification Using the Tree-based Approach." Thesis, Karlstads universitet, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-43012.

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This dissertation describes a study which aims to classify video flows from Internet network traffic. In this study, classification is done based on the characteristics of the flow, which includes features such as payload sizes and inter-arrival time. The purpose of this is to give an alternative to classifying flows based on the contents of their payload packets. Because of an increase of encrypted flows within Internet network traffic, this is a necessity. Data with known class is fed to a machine learning classifier such that a model can be created. This model can then be used for classific
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Nemčíková, Lucia. "Classification and Regression Trees in R." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-194149.

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Tree-based methods are a nice add-on to traditional statistical methods when solving classification and regression problems. The aim of this master thesis is not to judge which approach is better but rather bring the overview of these methods and apply them on the real data using R. Focus is made especially on the basic methodology of tree-based models and the application in specific software in order to provide wide range of tool for reader to be able to use these methods. One part of the thesis touches the advanced tree-based methods to provide full picture of possibilities.
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Jin, Wei. "Blog comments classification using tree structured conditional random fields." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/43571.

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The Internet provides a variety of ways for people to easily share, socialize, and interact with each other. One of the most popular platforms is the online blog. This causes a vast amount of new text data in the form of blog comments and opinions about news, events and products being generated everyday. However, not all comments have equal quality. Informative or high quality comments have greater impact on the readers’ opinions about the original post content, such as the benefits of the product discussed in the post, or the interpretation of a political event. Therefore, developing an effi
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MARQUES, DANIEL DOS SANTOS. "A DECISION TREE LEARNER FOR COST-SENSITIVE BINARY CLASSIFICATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=28239@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Problemas de classificação foram amplamente estudados na literatura de aprendizado de máquina, gerando aplicações em diversas áreas. No entanto, em diversos cenários, custos por erro de classificação podem variar bastante, o que motiva o estudo de técnicas de classificação sensível ao custo. Nesse trabalho, discutimos o uso de árvores de decisão para o problema mais geral de Aprendizado Sensível ao Custo do Exemplo (ASCE), onde os custos dos erros de classificação variam com o
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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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Pavuluri, Manoj Kumar. "Fuzzy decision tree classification for high-resolution satellite imagery /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p1418056.

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Chen, Chia-sheng. "Using neural networks within the leaves of a classification tree." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://handle.dtic.mil/100.2/ADA380713.

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Hedberg, Rikard. "Tree species classification using support vector machine on hyperspectral images." Thesis, Linköping University, Department of Electrical Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54648.

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<p>For several years, FORAN Remote Sensing in Linköping has been using pulseintense laser scannings together with multispectral imaging for developing analysismethods in forestry. One area these laser scannings and images are used for is toclassify the species of single trees in forests. The species have been divided intopine, spruce and deciduous trees, classified by a Maximum Likelihood classifier.This thesis presents the work done on a more spectrally high-resolution imagery,hyperspectral images. These images are divided into more, and finer gradedspectral components, but demand more signal
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Lundkvist, Emil. "Decision Tree Classification and Forecasting of Pricing Time Series Data." Thesis, KTH, Reglerteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-151017.

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Many companies today, in different fields of operations and sizes, have access to a vast amount of data which was not available only a couple of years ago. This situation gives rise to questions regarding how to organize and use the data in the best way possible. In this thesis a large database of pricing data for products within various market segments is analysed. The pricing data is from both external and internal sources and is therefore confidential. Because of the confidentiality, the labels from the database are in this thesis substituted with generic ones and the company is not referre
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Books on the topic "Classification tree"

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Joel, Cracraft, and Donoghue Michael J, eds. Assembling the tree of life. Oxford University Press, 2004.

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Gibbons, Gail. Tell me, tree: All about trees for kids. Little, Brown, 2002.

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D, Grill, ed. Special issue Tree growth at high altitude and high latitude: COST - action E6, EUROSILVA, forest tree physiology research. Berger, 1999.

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Workshop on Taxonomy and Seed Handling of Australian Tree Species in Eastern and Southern Africa (1985 Harare, Zimbabwe). Proceedings of a Workshop on Taxonomy and Seed Handling of Australian Tree Species in Eastern and Southern Africa, Harare, Zimbabwe, 08-12 July 1985. Edited by Ayling R. D, Seward B. R. T, International Development Research Centre (Canada), and Zimbabwe Forestry Commission. Forestry Commission of Zimbabwe, 1986.

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Barneby, Rupert C. Silk tree, Guanacaste, Monkey's earring: A generic system for the synandrous Mimosaceae of the Americas. New York Botanical Garden, 1996.

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R, Hodkinson Trevor, and Parnell John A. N, eds. Reconstructing the tree of life: Taxonomy and systematics of species rich taxa. CRC Press, 2007.

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Sapp, Jan. The new foundations of evolution: On the tree of life. Oxford University Press, 2009.

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May, Dennis M. New tree-classification system used by the Southern Forest Inventory and Analysis Unit. U.S. Dept. of Agriculture, Forest Service, Southern Forest Experiment Station, 1990.

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May, Dennis M. New tree-classification system used by the Southern Forest Inventory and Analysis Unit. U.S. Dept. of Agriculture, Forest Service, Southern Forest Experiment Station, 1990.

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May, Dennis M. New tree-classification system used by the Southern Forest Inventory and Analysis Unit. U.S. Dept. of Agriculture, Forest Service, Southern Forest Experiment Station, 1990.

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Book chapters on the topic "Classification tree"

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Shultz, Thomas R., Scott E. Fahlman, Susan Craw, et al. "Classification Tree." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_114.

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Dobra, Alin. "Decision Tree Classification." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_554-2.

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Dobra, Alin. "Decision Tree Classification." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_554.

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Dobra, Alin. "Decision Tree Classification." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_554.

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Lerman, Israël César. "Building a Classification Tree." In Advanced Information and Knowledge Processing. Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-6793-8_10.

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Schallau, Till, Stefan Naujokat, Fiona Kullmann, and Falk Howar. "Tree-Based Scenario Classification." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60698-4_15.

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Suthaharan, Shan. "Decision Tree Learning." In Machine Learning Models and Algorithms for Big Data Classification. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7641-3_10.

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Suthaharan, Shan. "Chandelier Decision Tree." In Machine Learning Models and Algorithms for Big Data Classification. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7641-3_13.

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Leclerc, Bruno. "Adjustments of tree metrics based on minimum spanning trees." In New Approaches in Classification and Data Analysis. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-51175-2_13.

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Kalita, Jugal. "Tree-Based Classification and Regression." In Machine Learning. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003002611-3.

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Conference papers on the topic "Classification tree"

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Manoharan, Sakthiprasad Kuttankulungara, Rajesh Kannan Megalingam, and Sruthi Padathiparambil Sreeji. "Machine Learning Based Classification of Coconut Trees Based on Tree Parameters." In 2024 2nd World Conference on Communication & Computing (WCONF). IEEE, 2024. http://dx.doi.org/10.1109/wconf61366.2024.10692268.

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Binder, Edgar, and Manfred Kufleitner. "Classification tree Sources." In 2009 IEEE Information Theory Workshop. IEEE, 2009. http://dx.doi.org/10.1109/itw.2009.5351448.

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Moghadam, Armin, and Fatemeh Davoudi Kakhki. "Comparative Study of Decision Tree Models for Bearing Fault Detection and Classification." In Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe100968.

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Fault diagnosis of bearings is essential in reducing failures and improving functionality and reliability of rotating machines. As vibration signals are non-linear and non-stationary, extracting features for dimension reduction and efficient fault detection is challenging. This study aims at evaluating performance of decision tree-based machine learning models in detection and classification of bearing fault data. A machine learning approach combining the tree-based classifiers with derived statistical features is proposed for localized fault classification. Statistical features are extracted
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Jiang, Yi, and Fengjun Shang. "Research on Multibit-Trie Tree IP Classification Algorithm." In 2006 International Conference on Communications, Circuits and Systems. IEEE, 2006. http://dx.doi.org/10.1109/icccas.2006.285020.

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Al-Khudafi, Abbas M., Hamzah A. Al-Sharifi, Ghareb M. Hamada, Mohamed A. Bamaga, Abdulrahman A. Kadi, and 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.

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Abstract This study aims to assess the effectiveness of several decision tree techniques for identifying formation lithology. 20966 data points from 4 wells were used to create the study's data. Lithology is determined using seven log parameters. The seven log parameters are the density log, neutron log, sonic log, gamma ray log, deep latero log, shallow latero log, and resistivity log. Different decision tree-based algorithms for classification approaches were applied. six typical machine learning models, namely the, Random Forest. Random trees, J48, reduced-error pruning decision trees, logi
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Pongsomsong, Panattha, and Paruj Ratanaworabhan. "Automatic Rubber Tree Classification." In 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2021. http://dx.doi.org/10.1109/ecti-con51831.2021.9454800.

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Yoshii, H. "Pyramid architecture classification tree." In Proceedings of 13th International Conference on Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/icpr.1996.546839.

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Lu, Wanping, Wei Li, Lingfeng Wang, and Chunhong Pan. "Gestures Classification Based on Semantic Classification Tree." In 2009 2nd International Congress on Image and Signal Processing (CISP). IEEE, 2009. http://dx.doi.org/10.1109/cisp.2009.5304557.

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Haoliang, Sun, and Wang Dawei. "Multi-step Trie Tree Packet Classification Method supporting Wildcards." In 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2019. http://dx.doi.org/10.1109/imcec46724.2019.8984152.

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Itakura, Kenta, Teruhito Hata, and Fumiki Hosoi. "Tree Species Classification Using Leaf and Tree Trunk Images." In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9324126.

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Reports on the topic "Classification tree"

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Gollapinni, Sowjanya, Daniel Xing, and Mark Ross-Lonergan. An Improved Boosted Decision Tree Framework for Event Classification in MicroBooNE. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2340859.

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May, Dennis M., John S. Vissage, and D. Vince Few. New Tree-Classification System Used by the Southern Forest Inventory and Analysis Unit. U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station, 1990. http://dx.doi.org/10.2737/so-gtr-076.

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May, Dennis M., John S. Vissage, and D. Vince Few. New Tree-Classification System Used by the Southern Forest Inventory and Analysis Unit. U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station, 1990. http://dx.doi.org/10.2737/so-gtr-76.

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Majd, Farjam. Two new parallel processors for real time classification of 3-D moving objects and quad tree generation. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.5301.

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Eckert, Richard. PR-186-184509-R01 Guideline for Erosional Velocity. Pipeline Research Council International, Inc. (PRCI), 2020. http://dx.doi.org/10.55274/r0011655.

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A guideline to determine erosional velocity limits for liquid hydrocarbon transmission pipelines was developed based on a multi-analytical probabilistic approach that integrated results from two erosional models: DNV GL RP-O501 and University of Tulsa SPPS v5.3. The guideline uses a simple classification tree model as first approach to provide conservative erosional velocities with a minimum amount of input data. The guideline also presents an alternative probabilistic approach for determining erosional velocities when the classification tree cannot be used, e.g., when there is too much data u
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Zio, Enrico, and Nicola Pedroni. Uncertainty characterization in risk analysis for decision-making practice. Fondation pour une culture de sécurité industrielle, 2012. http://dx.doi.org/10.57071/155chr.

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This document provides an overview of sources of uncertainty in probabilistic risk analysis. For each phase of the risk analysis process (system modeling, hazard identification, estimation of the probability and consequences of accident sequences, risk evaluation), the authors describe and classify the types of uncertainty that can arise. The document provides: a description of the risk assessment process, as used in hazardous industries such as nuclear power and offshore oil and gas extraction; a classification of sources of uncertainty (both epistemic and aleatory) and a description of techn
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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no
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Asher, Sam, Denis Nekipelov, Paul Novosad, and Stephen Ryan. Classification Trees for Heterogeneous Moment-Based Models. National Bureau of Economic Research, 2016. http://dx.doi.org/10.3386/w22976.

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Abell, Caitlyn E., Anna K. Johnson, Locke A. Karriker, Suzanne T. Millman, and Kenneth J. Stalder. Using Classification Trees to Detect Lameness in Sows. Iowa State University, 2013. http://dx.doi.org/10.31274/ans_air-180814-930.

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Holmes, Robert M., and Chester Pabiniak. Forecasting PCS (Permanent Change of Station ORT Moves Using Tree Classifications. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada213235.

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