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Journal articles on the topic 'Classification tree'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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Povkhan, I. F. "CONVERGENCE PROBLEM SCHEMES FOR CONSTRUCTING STRUCTURES OF LOGICAL AND ALGORITHMIC CLASSIFICATION TREES." Ukrainian Journal of Information Technology 4, no. 1 (2022): 29–36. http://dx.doi.org/10.23939/ujit2022.01.029.

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The problem of convergence of the procedure for synthesizing classifier schemes in the methods of logical and algorithmic classification trees is considered. An upper estimate of the complexity of the algorithm tree scheme is proposed in the problem of approximating an array of real data with a set of generalized features with a fixed criterion for stopping the branching procedure at the stage of constructing a classification tree. This approach allows you to ensure the necessary accuracy of the model, assess its complexity, reduce the number of branches and achieve the necessary performance i

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Salsabila, Alifia Salwa, Christy Atika Sari, and Eko Hari Rachmawanto. "Classification of Movie Recommendation on Netflix Using Random Forest Algorithm." Advance Sustainable Science Engineering and Technology 6, no. 3 (2024): 02403016. http://dx.doi.org/10.26877/asset.v6i3.676.

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Netflix is one of the most popular streaming platforms in this world. So many movies and shows with various genres and production countries are available on this platform. Netflix has their own recommendation systems for the subscribers according to their data and algorithm. This research aims to compare two methods of data classifications using Decision Tree and Random Forest algorithm and make a recommendation system based on Netflix dataset. This paper use feature importance to selecting relevant feature and how n_estimators affect the classification. In this research, Random Forest with 50

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Povkhan, I. F. "METHOD FOR SYNTHESIZING LOGICAL CLASSIFICATION TREES BASED ON THE SELECTION OF ELEMENTARY FEATURES." Ukrainian Journal of Information Technology 4, no. 2 (2022): 25–32. http://dx.doi.org/10.23939/ujit2022.02.025.

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The general problem of constructing logical recognition and classification trees is considered. The object of this study is logical classification trees. The subject of the research is current methods and algorithms for constructing logical classification trees. The aim of the work is to create a simple and effective method for constructing recognition models based on classification trees for training samples of discrete information, which is characterized by elementary features in the structure of synthesized logical classification trees. A general method for constructing logical classificati

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Cai, Yuliang, Huaguang Zhang, Qiang He, and Shaoxin Sun. "New classification technique: fuzzy oblique decision tree." Transactions of the Institute of Measurement and Control 41, no. 8 (2018): 2185–95. http://dx.doi.org/10.1177/0142331218774614.

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Based on axiomatic fuzzy set (AFS) theory and fuzzy information entropy, a novel fuzzy oblique decision tree (FODT) algorithm is proposed in this paper. Traditional axis-parallel decision trees only consider a single feature at each non-leaf node, while oblique decision trees partition the feature space with an oblique hyperplane. By contrast, the FODT takes dynamic mining fuzzy rules as a decision function. The main idea of the FODT is to use these fuzzy rules to construct leaf nodes for each class in each layer of the tree; the samples that cannot be covered by the fuzzy rules are then put i

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Naveen Kumar, Nallamothu. "Model of Decision Tree for Email Classification." International Journal of Science and Research (IJSR) 11, no. 7 (2022): 1502–5. http://dx.doi.org/10.21275/sr22722110223.

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Meadows, James S., and Daniel A. Skojac. "A New Tree Classification System for Southern Hardwoods." Southern Journal of Applied Forestry 32, no. 2 (2008): 69–79. http://dx.doi.org/10.1093/sjaf/32.2.69.

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Abstract A new tree classification system for southern hardwoods is described. The new system is based on the Putnam tree classification system, originally developed by Putnam et al., 1960, Management and inventory of southern hardwoods, Agriculture Handbook 181, US For. Serv., Washington, C, which consists of four tree classes: (1) preferred growing stock, (2) reserve growing stock, (3) cutting stock, and (4) cull stock. Tree classes under the Putnam system are used as a basis for planning partial cuttings and for developing marking rules in southern hardwood forests. However, there are criti

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Ampuła, Dariusz. "Decision Trees in the Tests of Artillery Igniters." Journal of KONBiN 50, no. 1 (2020): 95–116. http://dx.doi.org/10.2478/jok-2020-0007.

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AbstractThe article addressed the method for building decision trees paying attention to the binary character of the tree structure. The methodology for building our decision tree for KW-4 igniters was presented. It involves determining features of tested igniters and applied predictors, which are necessary to create the correct model of the tree. The classification tree was built based on the possessed test results, determining the adopted post-diagnostic decision as the qualitative independent variable. The schema of the resultant classification tree and the full structure of this tree toget

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He, Yuhong, Jian Yang, John Caspersen, and Trevor Jones. "An Operational Workflow of Deciduous-Dominated Forest Species Classification: Crown Delineation, Gap Elimination, and Object-Based Classification." Remote Sensing 11, no. 18 (2019): 2078. http://dx.doi.org/10.3390/rs11182078.

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Recent advances in remote sensing technology provide sufficient spatial detail to achieve species-level classification over large vegetative ecosystems. In deciduous-dominated forests, however, as tree species diversity and forest structural diversity increase, the frequency of spectral overlap between species also increases and our ability to classify tree species significantly decreases. This study proposes an operational workflow of individual tree-based species classification for a temperate, mixed deciduous forest using three-seasonal WorldView images, involving three steps of individual

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Buschjäger, Sebastian, Sibylle Hess, and Katharina J. Morik. "Shrub Ensembles for Online Classification." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6123–31. http://dx.doi.org/10.1609/aaai.v36i6.20560.

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Online learning algorithms have become a ubiquitous tool in the machine learning toolbox and are frequently used in small, resource-constraint environments. Among the most successful online learning methods are Decision Tree (DT) ensembles. DT ensembles provide excellent performance while adapting to changes in the data, but they are not resource efficient. Incremental tree learners keep adding new nodes to the tree but never remove old ones increasing the memory consumption over time. Gradient-based tree learning, on the other hand, requires the computation of gradients over the entire tree w

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Maschler, Julia, Clement Atzberger, and Markus Immitzer. "Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data." Remote Sensing 10, no. 8 (2018): 1218. http://dx.doi.org/10.3390/rs10081218.

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Knowledge of the distribution of tree species within a forest is key for multiple economic and ecological applications. This information is traditionally acquired through time-consuming and thereby expensive field work. Our study evaluates the suitability of a visible to near-infrared (VNIR) hyperspectral dataset with a spatial resolution of 0.4 m for the classification of 13 tree species (8 broadleaf, 5 coniferous) on an individual tree crown level in the UNESCO Biosphere Reserve ‘Wienerwald’, a temperate Austrian forest. The study also assesses the automation potential for the delineation of

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Povkhan, I. F. "THE METHOD OF BOUNDED CONSTRUCTIONS OF LOGICAL CLASSIFICATION TREES IN THE PROBLEM OF DISCRETE OBJECTS CLASSIFICATION." Ukrainian Journal of Information Technology 3, no. 1 (2021): 22–29. http://dx.doi.org/10.23939/ujit2021.03.022.

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The problem of constructing a model of logical classification trees based on a limited method of selecting elementary features for geological data arrays is considered. A method for approximating an array of real data with a set of elementary features with a fixed criterion for stopping the branching procedure at the stage of constructing a classification tree is proposed. This approach allows to ensure the necessary accuracy of the model, reduce its structural complexity, and achieve the necessary performance indicators. A limited method for constructing classification trees has been develope

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Doi, Hideyuki, and Tomoki Hirai. "Estimation of deracinated trees area in temperate forest with satellite images employing machine learning methods." PeerJ Computer Science 7 (August 19, 2021): e648. http://dx.doi.org/10.7717/peerj-cs.648.

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Climate change can increase the number of uprooted trees. Although there have been an increasing number of machine learning applications for satellite image analysis, the estimation of deracinated tree area by satellite image is not well developed. Therefore, we estimated the deracinated tree area of forests via machine-learning classification using Landsat 8 satellite images. We employed support vector machines (SVMs), random forests (RF), and convolutional neural networks (CNNs) as potential machine learning methods, and tested their performance in estimating the deracinated tree area. We co

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Dudnyk, V., O. Grishchyn, V. Netrebko, R. Prus, and M. Voloshcuk. "Methodical approach to assess the condition of samples of weapons and military equipment on the basis of algorithmic trees." Military Technical Collection, no. 25 (December 8, 2021): 69–76. http://dx.doi.org/10.33577/2312-4458.25.2021.69-76.

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An effective mechanism for the synthesis of classification trees based on fixed initial information (in the form of a training sample) for the task of recognizing the technical condition of samples of weapons and military equipment. The constructed algorithmic classification tree (model) will unmistakably classify (recognize) the entire training sample (situational objects) according to which the classification scheme is constructed. And have a minimal structure (structural complexity) and consist of components (modules) - autonomous algorithms for classification and recognition as vertices of

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Povhan, Igor. "LOGICAL CLASSIFICATION TREES IN RECOGNITION PROBLEMS." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 10, no. 2 (2020): 12–15. http://dx.doi.org/10.35784/iapgos.927.

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The paper is dedicated to algorithms for constructing a logical tree of classification. Nowadays, there exist many algorithms for constructing logical classification trees. However, all of them, as a rule, are reduced to the construction of a single classification tree based on the data of a fixed training sample. There are very few algorithms for constructing recognition trees that are designed for large data sets. It is obvious that such sets have objective factors associated with the peculiarities of the generation of such complex structures, methods of working with them and storage. In thi

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Choromanska, Anna, and Ish Kumar Jain. "Extreme Multiclass Classification Criteria." Computation 7, no. 1 (2019): 16. http://dx.doi.org/10.3390/computation7010016.

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We analyze the theoretical properties of the recently proposed objective function for efficient online construction and training of multiclass classification trees in the settings where the label space is very large. We show the important properties of this objective and provide a complete proof that maximizing it simultaneously encourages balanced trees and improves the purity of the class distributions at subsequent levels in the tree. We further explore its connection to the three well-known entropy-based decision tree criteria, i.e., Shannon entropy, Gini-entropy and its modified variant,

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Aung, Nway Oo, and Naing Thin. "Decision Tree Models for Medical Diagnosis." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 1697–99. https://doi.org/10.31142/ijtsrd23510.

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Data mining techniques are rapidly developed for many applications. In recent year, Data mining in healthcare is an emerging field research and development of intelligent medical diagnosis system. Classification is the major research topic in data mining. Decision trees are popular methods for classification. In this paper many decision tree classifiers are used for diagnosis of medical datasets. AD Tree, J48, NB Tree, Random Tree and Random Forest algorithms are used for analysis of medical dataset. Heart disease dataset, Diabetes dataset and Hepatitis disorder dataset are used to test the de

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Briechle, S., P. Krzystek, and G. Vosselman. "CLASSIFICATION OF TREE SPECIES AND STANDING DEAD TREES BY FUSING UAV-BASED LIDAR DATA AND MULTISPECTRAL IMAGERY IN THE 3D DEEP NEURAL NETWORK POINTNET++." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 203–10. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-203-2020.

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Abstract. Knowledge of tree species mapping and of dead wood in particular is fundamental to managing our forests. Although individual tree-based approaches using lidar can successfully distinguish between deciduous and coniferous trees, the classification of multiple tree species is still limited in accuracy. Moreover, the combined mapping of standing dead trees after pest infestation is becoming increasingly important. New deep learning methods outperform baseline machine learning approaches and promise a significant accuracy gain for tree mapping. In this study, we performed a classificatio

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Katz, Daniel S. W., Stuart A. Batterman, and Shannon J. Brines. "Improved Classification of Urban Trees Using a Widespread Multi-Temporal Aerial Image Dataset." Remote Sensing 12, no. 15 (2020): 2475. http://dx.doi.org/10.3390/rs12152475.

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Urban tree identification is often limited by the accessibility of remote sensing imagery but has not yet been attempted with the multi-temporal commercial aerial photography that is now widely available. In this study, trees in Detroit, Michigan, USA are identified using eight high resolution red, green, and blue (RGB) aerial images from a commercial vendor and publicly available LiDAR data. Classifications based on these data were compared with classifications based on World View 2 satellite imagery, which is commonly used for this task but also more expensive. An object-based classification

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Hasan, Ahmad Abdulsalam Naji. "Coffee Tree Detection Using Convolutional Neural Network." International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) 3, no. 2 (2022): 231–40. http://dx.doi.org/10.34010/injiiscom.v3i2.9504.

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Identifying plants is an important field in the environment because of their roles in the continuation of human existence. Finding a plant by using the traditional methods such as looking at its physical properties is a burdensome task. Thus, several computational-based methods have been introduced for detecting trees. In this study we constructed the coffee tree dataset due there is no publicly available coffee tree dataset for detection and classification of the coffee tree in orchard environments for what this tree has a role in health, industrial and agricultural fields, and raising the wh

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S.V.S., Ganga Devi. "FUZZY RULE EXTRACTION FOR FRUIT DATA CLASSIFICATION." COMPUSOFT: An International Journal of Advanced Computer Technology 02, no. 12 (2013): 400–403. https://doi.org/10.5281/zenodo.14613549.

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Decision Tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic Decision Tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible Decision Trees have been designed and then rules are extracted for perfect symbolic data. Over the years, additional methodologies have been investigated and proposed to deal with continuous or multi-valued data and with missing or noisy features. Recently, with the growing popularity of fuzzy representation in Decision Trees are introd

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Suwardika, Gede Suwardika, and I. Ketut Putu Suniantara. "ANALISIS RANDOM FOREST PADA KLASIFIKASI CART KETIDAKTEPATAN WAKTU KELULUSAN MAHASISWA UNIVERSITAS TERBUKA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 13, no. 3 (2019): 177–84. http://dx.doi.org/10.30598/barekengvol13iss3pp177-184ar910.

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Classification and Regression Tree (CART) is one of the classification methods that are popularly used in various fields. The method is considered capable of dealing with various data conditions. However, the CART method has weaknesses in the classification tree prediction, which is less stable in changes in learning data which will cause major changes in the results of the classification tree prediction. Improving the predictions of the CART classification tree, an ensemble random forest method was developed that combines many classification trees to improve stability and determine classifica

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Man, Qixia, Xinming Yang, Haijian Liu, et al. "Comparison of UAV-Based LiDAR and Photogrammetric Point Cloud for Individual Tree Species Classification of Urban Areas." Remote Sensing 17, no. 7 (2025): 1212. https://doi.org/10.3390/rs17071212.

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UAV LiDAR and digital aerial photogrammetry (DAP) have shown great performance in forest inventory due to their advantage in three-dimensional information extraction. Many studies have compared their performance in individual tree segmentation and structural parameters extraction (e.g. tree height). However, few studies have compared their performance in tree species classification. Therefore, we have compared the performance of UAV LiDAR and DAP-based point clouds in individual tree species classification with the following steps: (1) Point cloud data processing: Denoising, smoothing, and nor

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Meng, Yuan, Xibin Dong, Kaili Han, Hui Liu, Hangfeng Qu, and Tong Gao. "Classification of Tree Species Using Point Cloud Features from Terrestrial Laser Scanning." Forests 15, no. 12 (2024): 2110. http://dx.doi.org/10.3390/f15122110.

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The automatic classification of tree species using terrestrial laser scanning (TLS) point clouds is key in forestry research. This study aims to develop a robust framework for tree species classification by integrating advanced feature extraction and machine learning techniques. Such a framework is of great significance for investigating and monitoring forest resources, sustainable forest management, and biodiversity research. To achieve this, point cloud data from 360 trees of four species were collected at the Northeastern Forestry University in Harbin City, Heilongjiang Province. Three type

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Sumsion, G. Rex, Michael S. Bradshaw, Kimball T. Hill, Lucas D. G. Pinto, and Stephen R. Piccolo. "Remote sensing tree classification with a multilayer perceptron." PeerJ 7 (February 28, 2019): e6101. http://dx.doi.org/10.7717/peerj.6101.

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To accelerate scientific progress on remote tree classification—as well as biodiversity and ecology sampling—The National Institute of Science and Technology created a community-based competition where scientists were invited to contribute informatics methods for classifying tree species and genus using crown-level images of trees. We classified tree species and genus at the pixel level using hyperspectral and LiDAR observations. We compared three algorithms that have been implemented extensively across a broad range of research applications: support vector machines, random forests, and multil

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Guillemette, François, Steve Bédard, and Mathieu Fortin. "Evaluation of a tree classification system in relation to mortality risk in Québec northern hardwoods." Forestry Chronicle 84, no. 6 (2008): 886–99. http://dx.doi.org/10.5558/tfc84886-6.

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A tree classification system was developed in the 1980s as part of a guide for tree-marking in the rehabilitation of unevenaged northern hardwood stands in Québec. It differentiates trees that are at high and low risk of mortality, trees with sawlog potential and cull trees. The risk class was assessed based on the presence of major crown and bole defects. The main objective of the present study was to evaluate this system with respect to its capacity to predict the probability of tree mortality. The variables used to classify the trees were observed in 88 experimental plots (0.5 ha) establish

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Ma, Minfei, Jianhong Liu, Mingxing Liu, Jingchao Zeng, and Yuanhui Li. "Tree Species Classification Based on Sentinel-2 Imagery and Random Forest Classifier in the Eastern Regions of the Qilian Mountains." Forests 12, no. 12 (2021): 1736. http://dx.doi.org/10.3390/f12121736.

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Obtaining accurate forest coverage of tree species is an important basis for the rational use and protection of existing forest resources. However, most current studies have mainly focused on broad tree classification, such as coniferous vs. broadleaf tree species, and a refined tree classification with tree species information is urgently needed. Although airborne LiDAR data or unmanned aerial vehicle (UAV) images can be used to acquire tree information even at the single tree level, this method will encounter great difficulties when applied to a large area. Therefore, this study takes the ea

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Tang, Hongying, Fan Miao, Jie Yang, Bingyu Wu, Qi Zhang, and Hongke Hao. "Considering the composite tree attributes extracted by UAV can improve the accuracy of street tree species classification." Dendrobiology 91 (May 6, 2024): 85–99. http://dx.doi.org/10.12657/denbio.091.007.

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Identifying simple tree attributes of street trees, i.e., tree height, crown width and crown height obtained by unmanned aerial vehicles (UAV), plays a significant role in urban management to maximize the ecological benefits of street trees. However, simple attributes usually fluctuate over a wide range due to differences in tree-age and growing environment, leading to inconspicuous interspecific features and low classification accuracy. Composite attributes, expressed by two or more simple attributes, can be used to reduce the variability in simple tree attributes, thus providing an alternati

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Ni-Meister, Wenge, Anthony Albanese, and Francesca Lingo. "Assessing Data Preparation and Machine Learning for Tree Species Classification Using Hyperspectral Imagery." Remote Sensing 16, no. 17 (2024): 3313. http://dx.doi.org/10.3390/rs16173313.

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Tree species classification using hyperspectral imagery shows incredible promise in developing a large-scale, high-resolution model for identifying tree species, providing unprecedented details on global tree species distribution. Many questions remain unanswered about the best practices for creating a global, general hyperspectral tree species classification model. This study aims to address three key issues in creating a hyperspectral species classification model. We assessed the effectiveness of three data-labeling methods to create training data, three data-splitting methods for training/v

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Liu, Bingjie, Huaguo Huang, Yong Su, et al. "Tree Species Classification Using Ground-Based LiDAR Data by Various Point Cloud Deep Learning Methods." Remote Sensing 14, no. 22 (2022): 5733. http://dx.doi.org/10.3390/rs14225733.

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Tree species information is an important factor in forest resource surveys, and light detection and ranging (LiDAR), as a new technical tool for forest resource surveys, can quickly obtain the 3D structural information of trees. In particular, the rapid and accurate classification and identification of tree species information from individual tree point clouds using deep learning methods is a new development direction for LiDAR technology in forest applications. In this study, mobile laser scanning (MLS) data collected in the field are first pre-processed to extract individual tree point cloud

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Sirmacek, B., and R. Lindenbergh. "AUTOMATIC CLASSIFICATION OF TREES FROM LASER SCANNING POINT CLOUDS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W5 (August 19, 2015): 137–44. http://dx.doi.org/10.5194/isprsannals-ii-3-w5-137-2015.

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Development of laser scanning technologies has promoted tree monitoring studies to a new level, as the laser scanning point clouds enable accurate 3D measurements in a fast and environmental friendly manner. In this paper, we introduce a probability matrix computation based algorithm for automatically classifying laser scanning point clouds into ’tree’ and ’non-tree’ classes. Our method uses the 3D coordinates of the laser scanning points as input and generates a new point cloud which holds a label for each point indicating if it belongs to the ’tree’ or ’non-tree’ class. To do so, a grid surf

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Shokirov, Shukhrat, and Géza Király. "Analysis of multitemporal aerial images for fenyőfő Forest change detection." Landscape & Environment 10, no. 2 (2016): 89–100. http://dx.doi.org/10.21120/le/10/2/4.

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This study evaluated the use of 40 cm spatial resolution aerial images for individual tree crown delineation, forest type classification, health estimation and clear-cut area detection in Fenyőfő forest reserves in 2012 and 2015 years. Region growing algorithm was used for segmentation of individual tree crowns. Forest type (coniferous/deciduous trees) were distinguished based on the orthomosaic images and segments. Research also investigated the height of individual trees, clear-cut areas and cut crowns between 2012 and 2015 years using Canopy Height Models. Results of the research were exami

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Khoshgoftaar, Taghi M., and Naeem Seliya. "Software Quality Classification Modeling Using the SPRINT Decision Tree Algorithm." International Journal on Artificial Intelligence Tools 12, no. 03 (2003): 207–25. http://dx.doi.org/10.1142/s0218213003001204.

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Predicting the quality of system modules prior to software testing and operations can benefit the software development team. Such a timely reliability estimation can be used to direct cost-effective quality improvement efforts to the high-risk modules. Tree-based software quality classification models based on software metrics are used to predict whether a software module is fault-prone or not fault-prone. They are white box quality estimation models with good accuracy, and are simple and easy to interpret. An in-depth study of calibrating classification trees for software quality estimation u

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Suwardika, Gede, I. Ketut Putu Suniantara, and Ni Putu Nanik Hendayanti. "Ketidaktepatan Waktu Kelulusan Mahasiswa Universitas Terbuka dengan Metode Boosting Cart." Jurnal VARIAN 2, no. 2 (2019): 37–46. http://dx.doi.org/10.30812/varian.v2i2.361.

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The classification tree method or better known as Classification and Regression Tree (CART) has capabilities in various data conditions, but CART is less stable in changing learning data which will cause major changes in the results of the classification tree prediction. Predictive accuracy of an unstable classifier can be corrected by a combination method of many single classifiers where the prediction results of each classifier are combined into the final prediction through the majority voting process for classification or average voting for regression cases. Boosting ensemble method is one

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Iorio, Carmela, Massimo Aria, Antonio D'Ambrosio, and Roberta Siciliano. "Informative trees by visual pruning." Expert Systems with Applications 127 (August 1, 2019): 228–40. https://doi.org/10.1016/j.eswa.2019.03.018.

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The aim of this study is to provide visual pruning and decision tree selection for classification and regression trees. Specifically, we introduce an unedited tree graph to be made informative for recursive tree data partitioning. A decision tree is visually selected through a dendrogram-like procedure or through automatic tree-size selection. Our proposal is a one-step procedure whereby the most predictive paths are visualized. This method appears to be useful in all real world cases where tree-path interpretation is crucial. Experimental evaluations using real world data sets are presented.

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Sug, Hyontai. "Classification of Test Pads from Clustered PCB images." Engineering World 6 (December 10, 2024): 258–63. https://doi.org/10.37394/232025.2024.6.28.

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A robotic flying probe tester can be programmed to move the robotic probe to access all possible locations of test pads in a printed circuit board (PCB), and to record all connection test results like open or short circuits between all possible pairs of test pads in the board. For this purpose, Tan and Kit performed a clustering-based image cluster analysis on the photo image data of printed circuit boards to recover all test pad locations on the board and reported successful results. Their clustered data has been open to the public since 2024. So in this paper, several classification techniqu

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Khumaidi, Ali, Risanto Darmawan, and Diajeng Reztrianti. "Application of Ensemble Tree Algorithm for Installment Payment Arrears Prediction at Makmur Bersama Credit Union." Faktor Exacta 17, no. 2 (2024): 161. http://dx.doi.org/10.30998/faktorexacta.v17i2.21819.

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<span lang="EN-US">One of the widely used machine learning techniques is the ensemble tree method, which is a combination of several classification trees where the final decision is based on the combined predictions of each tree. This approach produces better accuracy than a single classification tree. Two common methods used in the ensemble tree technique are boosting and bagging. This research will predict the status of installment payments at CU Makmur Bersama Credit Union. The method used is the bagging tree method, namely random forest and boosting, namely AdaBoost. To get optimal r

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Kułaga, Rafał, and Marek Gorgoń. "FPGA Implementation of Decision Trees and Tree Ensembles for Character Recognition in Vivado Hls." Image Processing & Communications 19, no. 2-3 (2014): 71–82. http://dx.doi.org/10.1515/ipc-2015-0012.

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Abstract Decision trees and decision tree ensembles are popular machine learning methods, used for classification and regression. In this paper, an FPGA implementation of decision trees and tree ensembles for letter and digit recognition in Vivado High-Level Synthesis is presented. Two publicly available datasets were used at both training and testing stages. Different optimizations for tree code and tree node layout in memory are considered. Classification accuracy, throughput and resource usage for different training algorithms, tree depths and ensemble sizes are discussed. The correctness o

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Guan, H., Y. Yu, W. Yan, D. Li, and J. Li. "3D-CNN BASED TREE SPECIES CLASSIFICATION USING MOBILE LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 989–93. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-989-2019.

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<p><strong>Abstract.</strong> Our work addresses the problem of classifying tree species from mobile LiDAR data. The work is a two step-wise strategy, including tree segmentation and tree species classification. In the tree segmentation step, a voxel-based upward growing filtering is proposed to remove terrain points from the mobile laser scanning data. Then, individual trees are segmented via a Euclidean distance clustering approach and Voxel-based Normalized Cut (VNCut) segmentation approach. In the tree species classification, a voxel-based 3D convolutional neural network

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Ramadhani, Cahya Dwi, and Machrani Adi Putri Siregar. "CLASSIFICATION OF TODDLER NUTRITIONAL STATUS USING A BINARY CLASSIFICATION TREE WITH ALGORITHMSQUICK, UNBIASED, EFFICENT, STATISTICAL TREE." ZERO: Jurnal Sains, Matematika dan Terapan 7, no. 1 (2023): 43. http://dx.doi.org/10.30829/zero.v7i1.17339.

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<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Determination of nutritional status is very important in helping to monitor the state of nutritional health growth in toddlers every time. In this study, there were 70 identity data for toddlers for the 2022 period obtained from the KB Counseling Center in the Pegajahan sub-district, in Sukasari Village. There are four independent variables used, namely gender, health insurance, weight, and height. The purpose of this study is to determine the classification that is formed and th

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Zhang, Ju, Qingwu Hu, Hongyu Wu, Junying Su, and Pengcheng Zhao. "Application of Fractal Dimension of Terrestrial Laser Point Cloud in Classification of Independent Trees." Fractal and Fractional 5, no. 1 (2021): 14. http://dx.doi.org/10.3390/fractalfract5010014.

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Tree precise classification and identification of forest species is a core issue of forestry resource monitoring and ecological effect assessment. In this paper, an independent tree species classification method based on fractal features of terrestrial laser point cloud is proposed. Firstly, the terrestrial laser point cloud data of an independent tree is preprocessed to obtain terrestrial point clouds of independent tree canopy. Secondly, the multi-scale box-counting dimension calculation algorithm of independent tree canopy dense terrestrial laser point cloud is proposed. Furthermore, a robu

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