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Journal articles on the topic 'Classification tree models'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

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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Diligenti, M., P. Frasconi, and M. Gori. "Hidden tree markov models for document image classification." IEEE Transactions on Pattern Analysis and Machine Intelligence 25, no. 4 (2003): 520–24. http://dx.doi.org/10.1109/tpami.2003.1190578.

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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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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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Thoe, Wai, King Wah Choi, and Joseph Hun-wei Lee. "Predicting ‘very poor’ beach water quality gradings using classification tree." Journal of Water and Health 14, no. 1 (2015): 97–108. http://dx.doi.org/10.2166/wh.2015.094.

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A beach water quality prediction system has been developed in Hong Kong using multiple linear regression (MLR) models. However, linear models are found to be weak at capturing the infrequent ‘very poor’ water quality occasions when Escherichia coli (E. coli) concentration exceeds 610 counts/100 mL. This study uses a classification tree to increase the accuracy in predicting the ‘very poor’ water quality events at three Hong Kong beaches affected either by non-point source or point source pollution. Binary-output classification trees (to predict whether E. coli concentration exceeds 610 counts/

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Povkhan, Igor. "FEATURES OF SOFTWARE SOLUTIONS OF MODELS OF LOGICAL CLASSIFICATION TREES BASED ON SELECTION OF SETS OF ELEMENTARY FEATURES." Technical Sciences and Technologies, no. 4(22) (2020): 72–90. http://dx.doi.org/10.25140/2411-5363-2020-4(22)-72-90.

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Urgency of the research.Currently there are several independent approaches (concepts) to solve the classification problem in the general setting, and the development of various concepts, approaches, methods, and models that cover the general issues of the theory of artificial intelligence and information systems, all of these approaches in a recognition theory have their advantages and disadvantages and form a single tool to solve applied problems of the theory of artificial intelligence. This study will focus on the current concept of decision trees (classification trees). The general problem

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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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Hu, Ruo, and Zan Fu Xie. "Classification of Knowledge Discovery Methods." Applied Mechanics and Materials 63-64 (June 2011): 859–62. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.859.

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Knowledge Discovery, the science and technology of exploring knowledge in order to discover previously unknown patterns, is a part of the overall process of getting information in databases. In today’s computer-driven world, these databases contain a lot of information. The significant value of this information makes knowledge discovery a matter of considerable importance and necessity. A decision tree is a predictive model which can be used to represent both classifiers and regression models. When a decision tree is used for classification tasks, it is more appropriately referred to as a clas

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Thakkar, Pooja. "Drug Classification using Black-box models and Interpretability." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 1518–29. http://dx.doi.org/10.22214/ijraset.2021.38203.

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Abstract: The focus of this study is on drug categorization utilising Machine Learning models, as well as interpretability utilizing LIME and SHAP to get a thorough understanding of the ML models. To do this, the researchers used machine learning models such as random forest, decision tree, and logistic regression to classify drugs. Then, using LIME and SHAP, they determined if these models were interpretable, which allowed them to better understand their results. It may be stated at the conclusion of this paper that LIME and SHAP can be utilised to get insight into a Machine Learning model an

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Lim, Chee Soon, Edy Tonnizam Mohamad, Mohammad Reza Motahari, Danial Jahed Armaghani, and Rosli Saad. "Machine Learning Classifiers for Modeling Soil Characteristics by Geophysics Investigations: A Comparative Study." Applied Sciences 10, no. 17 (2020): 5734. http://dx.doi.org/10.3390/app10175734.

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To design geotechnical structures efficiently, it is important to examine soil’s physical properties. Therefore, classifying soil with respect to geophysical parameters is an advantageous and popular approach. Novel, quick, cost, and time effective machine learning techniques can facilitate this classification. This study employs three kinds of machine learning models, including the Decision Tree, Artificial Neural Networks, and Bayesian Networks. The Decision tree models included the chi-square automatic interaction detection (CHAID), classification and regression trees (CART), quick, unbiase

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Khoshgoftaar, T. M., E. B. Allen, W. D. Jones, and J. P. Hudepohl. "Classification-tree models of software-quality over multiple releases." IEEE Transactions on Reliability 49, no. 1 (2000): 4–11. http://dx.doi.org/10.1109/24.855532.

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LIANG, HAN, YUHONG YAN, and HARRY ZHANG. "LEARNING DECISION TREES WITH LOG CONDITIONAL LIKELIHOOD." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 01 (2010): 117–51. http://dx.doi.org/10.1142/s0218001410007877.

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In machine learning and data mining, traditional learning models aim for high classification accuracy. However, accurate class probability prediction is more desirable than classification accuracy in many practical applications, such as medical diagnosis. Although it is known that decision trees can be adapted to be class probability estimators in a variety of approaches, and the resulting models are uniformly called Probability Estimation Trees (PETs), the performances of these PETs in class probability estimation, have not yet been investigated. We begin our research by empirically studying

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Seo, Kanghyeon, Bokjin Chung, Hamsa Priya Panchaseelan, et al. "Forecasting the Walking Assistance Rehabilitation Level of Stroke Patients Using Artificial Intelligence." Diagnostics 11, no. 6 (2021): 1096. http://dx.doi.org/10.3390/diagnostics11061096.

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Cerebrovascular accidents (CVA) cause a range of impairments in coordination, such as a spectrum of walking impairments ranging from mild gait imbalance to complete loss of mobility. Patients with CVA need personalized approaches tailored to their degree of walking impairment for effective rehabilitation. This paper aims to evaluate the validity of using various machine learning (ML) and deep learning (DL) classification models (support vector machine, Decision Tree, Perceptron, Light Gradient Boosting Machine, AutoGluon, SuperTML, and TabNet) for automated classification of walking assistant

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Das, Adrian J., John J. Battles, Nathan L. Stephenson, and Phillip J. van Mantgem. "The relationship between tree growth patterns and likelihood of mortality: a study of two tree species in the Sierra Nevada." Canadian Journal of Forest Research 37, no. 3 (2007): 580–97. http://dx.doi.org/10.1139/x06-262.

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We examined mortality of Abies concolor (Gord. & Glend.) Lindl. (white fir) and Pinus lambertiana Dougl. (sugar pine) by developing logistic models using three growth indices obtained from tree rings: average growth, growth trend, and count of abrupt growth declines. For P. lambertiana, models with average growth, growth trend, and count of abrupt declines improved overall prediction (78.6% dead trees correctly classified, 83.7% live trees correctly classified) compared with a model with average recent growth alone (69.6% dead trees correctly classified, 67.3% live trees correctly classifi

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El-Rayes, Nesreen, Ming Fang, Michael Smith, and Stephen M. Taylor. "Predicting employee attrition using tree-based models." International Journal of Organizational Analysis 28, no. 6 (2020): 1273–91. http://dx.doi.org/10.1108/ijoa-10-2019-1903.

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Purpose The purpose of this study is to develop tree-based binary classification models to predict the likelihood of employee attrition based on firm cultural and management attributes. Design/methodology/approach A data set of resumes anonymously submitted through Glassdoor’s online portal is used in tandem with public company review information to fit decision tree, random forest and gradient boosted tree models to predict the probability of an employee leaving a firm during a job transition. Findings Random forest and decision tree methods are found to be the strongest attrition prediction

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Williams, Adina, Andrew Drozdov*, and Samuel R. Bowman. "Do latent tree learning models identify meaningful structure in sentences?" Transactions of the Association for Computational Linguistics 6 (December 2018): 253–67. http://dx.doi.org/10.1162/tacl_a_00019.

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Recent work on the problem of latent tree learning has made it possible to train neural networks that learn to both parse a sentence and use the resulting parse to interpret the sentence, all without exposure to ground-truth parse trees at training time. Surprisingly, these models often perform better at sentence understanding tasks than models that use parse trees from conventional parsers. This paper aims to investigate what these latent tree learning models learn. We replicate two such models in a shared codebase and find that (i) only one of these models outperforms conventional tree-struc

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Chuerubim, Maria Lígia, Alan Valejo, Barbara Stolte Bezerra, and Irineu Da Silva. "Limitation of classification tree models in investigating road accident severity." Revista de Engenharia Civil IMED 6, no. 2 (2019): 3. http://dx.doi.org/10.18256/2358-6508.2019.v6i2.2927.

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O objetivo deste estudo foi discutir as principais limitações encontradas no processo de classificação da severidade dos acidentes de tráfego, com base em modelos de árvore de decisão (CART). Para atingir este objetivo, a CART foi utilizada na mineração de um banco de dados desbalanceado de acidentes rodoviários, considerando a variável dependente severidade da lesão, a qual foi categorizada em acidentes sem vítimas e com vítimas (fatais e não fatais). Para tanto, foram utilizadas as variáveis associadas às características dos acidentes, à infraestrutura viária e às condições ambientais, com a

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Yan, Xuedong, and Essam Radwan. "Analyses of Rear-End Crashes Based on Classification Tree Models." Traffic Injury Prevention 7, no. 3 (2006): 276–82. http://dx.doi.org/10.1080/15389580600660062.

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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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Fan, Zhaofei, Stephen R. Shifley, Martin A. Spetich, Frank R. Thompson III, and David R. Larsen. "Distribution of cavity trees in midwestern old-growth and second-growth forests." Canadian Journal of Forest Research 33, no. 8 (2003): 1481–94. http://dx.doi.org/10.1139/x03-068.

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We used classification and regression tree analysis to determine the primary variables associated with the occurrence of cavity trees and the hierarchical structure among those variables. We applied that information to develop logistic models predicting cavity tree probability as a function of diameter, species group, and decay class. Inventories of cavity abundance in old-growth hardwood forests in Missouri, Illinois, and Indiana found that 811% of snags had at least one visible cavity (as visually detected from the ground; smallest opening [Formula: see text]2 cm diameter), about twice the

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Muzamil Basha, Syed, Dharmendra Singh Rajput, Ravi Kumar Poluru, S. Bharath Bhushan, and Shaik Abdul Khalandar Basha. "Evaluating the Performance of Supervised Classification Models: Decision Tree and Naïve Bayes Using KNIME." International Journal of Engineering & Technology 7, no. 4.5 (2018): 248. http://dx.doi.org/10.14419/ijet.v7i4.5.20079.

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The classification task is to predict the value of the target variable from the values of the input variables. If a target is provided as part of the dataset, then classification is a supervised task. It is important to analysis the performance of supervised classification models before using them in classification task. In our research we would like to propose a novel way to evaluated the performance of supervised classification models like Decision Tree and Naïve Bayes using KNIME Analytics platform. Experiments are conducted on Multi variant dataset consisting 58000 instances, 9 columns ass

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Begleiter, R., R. El-Yaniv, and G. Yona. "On Prediction Using Variable Order Markov Models." Journal of Artificial Intelligence Research 22 (December 1, 2004): 385–421. http://dx.doi.org/10.1613/jair.1491.

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This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent prediction algorithms, including Context Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic Suffix Trees (PSTs). We discuss the properties of these algorithms and compare their performance using real life sequences from three domains: proteins, English text and music pieces. The comparison is made with respect to

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Luna, José Marcio, Efstathios D. Gennatas, Lyle H. Ungar, et al. "Building more accurate decision trees with the additive tree." Proceedings of the National Academy of Sciences 116, no. 40 (2019): 19887–93. http://dx.doi.org/10.1073/pnas.1816748116.

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The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as tho

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Nai-Arun, Nongyao, and Punnee Sittidech. "Ensemble Learning Model for Diabetes Classification." Advanced Materials Research 931-932 (May 2014): 1427–31. http://dx.doi.org/10.4028/www.scientific.net/amr.931-932.1427.

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This paper proposed data mining techniques to improve efficiency and reliability in diabetes classification. The real data set collected from Sawanpracharak Regional Hospital, Thailand, was fist analyzed by using gain-ratio feature selection techniques. Three well known algorithms; naïve bayes, k-nearest neighbors and decision tree, were used to construct classification models on the selected features. Then, the popular ensemble learning; bagging and boosting were applied using the three base classifiers. The results revealed that the best model with the highest accuracy was bagging with base

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Ivanov, Atanas. "Decision Trees for Evaluation of Mathematical Competencies in the Higher Education: A Case Study." Mathematics 8, no. 5 (2020): 748. http://dx.doi.org/10.3390/math8050748.

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The assessment of knowledge and skills acquired by the student at each academic stage is crucial for every educational process. This paper proposes and tests an approach based on a structured assessment test for mathematical competencies in higher education and methods for statistical evaluation of the test. A case study is presented for the assessment of knowledge and skills for solving linear algebra and analytic geometry problems by first-year university students. The test includes three main parts—a multiple-choice test with four selectable answers, a solution of two problems with and with

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Grubb, Teryl G., and Rudy M. King. "Assessing Human Disturbance of Breeding Bald Eagles with Classification Tree Models." Journal of Wildlife Management 55, no. 3 (1991): 500. http://dx.doi.org/10.2307/3808982.

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Rizzo, Giuseppe, Claudia d’Amato, Nicola Fanizzi, and Floriana Esposito. "Tree-based models for inductive classification on the Web Of Data." Journal of Web Semantics 45 (August 2017): 1–22. http://dx.doi.org/10.1016/j.websem.2017.05.001.

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Fakir, Y., M. Azalmad, and R. Elaychi. "Study of The ID3 and C4.5 Learning Algorithms." Journal of Medical Informatics and Decision Making 1, no. 2 (2020): 29–43. http://dx.doi.org/10.14302/issn.2641-5526.jmid-20-3302.

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Data Mining is a process of exploring against large data to find patterns in decision-making. One of the techniques in decision-making is classification. Data classification is a form of data analysis used to extract models describing important data classes. There are many classification algorithms. Each classifier encompasses some algorithms in order to classify object into predefined classes. Decision Tree is one such important technique, which builds a tree structure by incrementally breaking down the datasets in smaller subsets. Decision Trees can be implemented by using popular algorithms

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Krasteva, Vessela, Irena Jekova, Remo Leber, Ramun Schmid, and Roger Abächerli. "Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System." PLOS ONE 10, no. 10 (2015): e0140123. http://dx.doi.org/10.1371/journal.pone.0140123.

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Currim, Imran S., Robert J. Meyer, and Nhan T. Le. "Disaggregate Tree-Structured Modeling of Consumer Choice Data." Journal of Marketing Research 25, no. 3 (1988): 253–65. http://dx.doi.org/10.1177/002224378802500303.

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A new approach to inferring hierarchical models of consumer choice is described. A classification algorithm is used to estimate decision trees at an individual level without requiring prior assumptions about tree form. Derived models are analyzed within a modeling system that summarizes the diversity of decision rules in a sample as well as their implications for aggregate market shares. An application to the analysis of panel data and a comparison with disaggregate logit analysis are reported.

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Abdollahnejad, Azadeh, and Dimitrios Panagiotidis. "Tree Species Classification and Health Status Assessment for a Mixed Broadleaf-Conifer Forest with UAS Multispectral Imaging." Remote Sensing 12, no. 22 (2020): 3722. http://dx.doi.org/10.3390/rs12223722.

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Automatic discrimination of tree species and identification of physiological stress imposed on forest trees by biotic factors from unmanned aerial systems (UAS) offers substantial advantages in forest management practices. In this study, we aimed to develop a novel workflow for facilitating tree species classification and the detection of healthy, unhealthy, and dead trees caused by bark beetle infestation using ultra-high resolution 5-band UAS bi-temporal aerial imagery in the Czech Republic. The study is divided into two steps. We initially classified the tree type, either as broadleaf or co

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Zurada, Jozef, Waldemar Karwowski, and William Marras. "Classification of jobs with risk of low back disorders by applying data mining techniques." Occupational Ergonomics 4, no. 4 (2005): 291–305. http://dx.doi.org/10.3233/oer-2004-4406.

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Work related low back disorders (LBDs) continue to pose significant occupational health problem that affects the quality of life of the industrial population. The main objective of this study was to explore the application of various data mining techniques, including neural networks, logistic regression, decision trees, memory-based reasoning, and the ensemble model, for classification of industrial jobs with respect to the risk of work-related LBDs. The results from extensive computer simulations using a 10-fold cross validation showed that memory-based reasoning and ensemble models were the

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Staňková, Michaela, and David Hampel. "Bankruptcy Prediction of Engineering Companies in the EU Using Classification Methods." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 66, no. 5 (2018): 1347–56. http://dx.doi.org/10.11118/actaun201866051347.

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This article focuses on the problem of binary classification of 902 small- and medium‑sized engineering companies active in the EU, together with additional 51 companies which went bankrupt in 2014. For classification purposes, the basic statistical method of logistic regression has been selected, together with a representative of machine learning (support vector machines and classification trees method) to construct models for bankruptcy prediction. Different settings have been tested for each method. Furthermore, the models were estimated based on complete data and also using identified arti

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Xie, Yunxin, Chenyang Zhu, Yue Lu, and Zhengwei Zhu. "Towards Optimization of Boosting Models for Formation Lithology Identification." Mathematical Problems in Engineering 2019 (August 14, 2019): 1–13. http://dx.doi.org/10.1155/2019/5309852.

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Lithology identification is an indispensable part in geological research and petroleum engineering study. In recent years, several mathematical approaches have been used to improve the accuracy of lithology classification. Based on our earlier work that assessed machine learning models on formation lithology classification, we optimize the boosting approaches to improve the classification ability of our boosting models with the data collected from the Daniudi gas field and Hangjinqi gas field. Three boosting models, namely, AdaBoost, Gradient Tree Boosting, and eXtreme Gradient Boosting, are e

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Carrizosa, Emilio, Cristina Molero-Río, and Dolores Romero Morales. "Mathematical optimization in classification and regression trees." TOP 29, no. 1 (2021): 5–33. http://dx.doi.org/10.1007/s11750-021-00594-1.

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AbstractClassification and regression trees, as well as their variants, are off-the-shelf methods in Machine Learning. In this paper, we review recent contributions within the Continuous Optimization and the Mixed-Integer Linear Optimization paradigms to develop novel formulations in this research area. We compare those in terms of the nature of the decision variables and the constraints required, as well as the optimization algorithms proposed. We illustrate how these powerful formulations enhance the flexibility of tree models, being better suited to incorporate desirable properties such as

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Zeng, Xiangxiang, Sisi Yuan, You Li, and Quan Zou. "Decision Tree Classification Model for Popularity Forecast of Chinese Colleges." Journal of Applied Mathematics 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/675806.

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Prospective students generally select their preferred college on the basis of popularity. Thus, this study uses survey data to build decision tree models for forecasting the popularity of a number of Chinese colleges in each district. We first extract a feature called “popularity change ratio” from existing data and then use a simplified but efficient algorithm based on “gain ratio” for decision tree construction. The final model is evaluated using common evaluation methods. This research is the first of its type in the educational field and represents a novel use of decision tree models with

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Uddameri, Venkatesh, Ana Silva, Sreeram Singaraju, Ghazal Mohammadi, and E. Hernandez. "Tree-Based Modeling Methods to Predict Nitrate Exceedances in the Ogallala Aquifer in Texas." Water 12, no. 4 (2020): 1023. http://dx.doi.org/10.3390/w12041023.

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The performance of four tree-based classification techniques—classification and regression trees (CART), multi-adaptive regression splines (MARS), random forests (RF) and gradient boosting trees (GBT) were compared against the commonly used logistic regression (LR) analysis to assess aquifer vulnerability in the Ogallala Aquifer of Texas. The results indicate that the tree-based models performed better than the logistic regression model, as they were able to locally refine nitrate exceedance probabilities. RF exhibited the best generalizable capabilities. The CART model did better in predictin

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Kumar, Sunil, Saroj Ratnoo, and Jyoti Vashishtha. "HYPER HEURISTIC EVOLUTIONARY APPROACH FOR CONSTRUCTING DECISION TREE CLASSIFIERS." Journal of Information and Communication Technology 20, Number 2 (2021): 249–76. http://dx.doi.org/10.32890/jict2021.20.2.5.

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Decision tree models have earned a special status in predictive modeling since these are considered comprehensible for human analysis and insight. Classification and Regression Tree (CART) algorithm is one of the renowned decision tree induction algorithms to address the classification as well as regression problems. Finding optimal values for the hyper parameters of a decision tree construction algorithm is a challenging issue. While making an effective decision tree classifier with high accuracy and comprehensibility, we need to address the question of setting optimal values for its hyper pa

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Saraee, M., B. Theodoulidis, J. A. Keane, and C. Tjortjis. "Using T3, an Improved Decision Tree Classifier, for Mining Stroke-related Medical Data." Methods of Information in Medicine 46, no. 05 (2007): 523–29. http://dx.doi.org/10.1160/me0317.

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Summary Objectives: Medical data are a valuable resource from which novel and potentially useful knowledge can be discovered by using data mining. Data mining can assist and support medical decision making and enhance clinical managementand investigative research. The objective of this work is to propose a method for building accurate descriptive and predictive models based on classification of past medical data. We also aim to compare this method with other well established data mining methods and identify strengths and weaknesses. Method: We propose T3, a decision tree classifier which build

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Egelberg, Jacob, Nina Pena, Rachel Rivera, and Christina Andruk. "Assessing the geographic specificity of pH prediction by classification and regression trees." PLOS ONE 16, no. 8 (2021): e0255119. http://dx.doi.org/10.1371/journal.pone.0255119.

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Soil pH effects a wide range of critical biogeochemical processes that dictate plant growth and diversity. Previous literature has established the capacity of classification and regression trees (CARTs) to predict soil pH, but limitations of CARTs in this context have not been fully explored. The current study collected soil pH, climatic, and topographic data from 100 locations across New York’s Temperate Deciduous Forests (in the United States of America) to investigate the extrapolative capacity of a previously developed CART model as compared to novel CART and random forest (RF) models. Res

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Quan, Zhiyu, and Emiliano A. Valdez. "Predictive analytics of insurance claims using multivariate decision trees." Dependence Modeling 6, no. 1 (2018): 377–407. http://dx.doi.org/10.1515/demo-2018-0022.

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AbstractBecause of its many advantages, the use of decision trees has become an increasingly popular alternative predictive tool for building classification and regression models. Its origins date back for about five decades where the algorithm can be broadly described by repeatedly partitioning the regions of the explanatory variables and thereby creating a tree-based model for predicting the response. Innovations to the original methods, such as random forests and gradient boosting, have further improved the capabilities of using decision trees as a predictive model. In addition, the extensi

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Behr, Andreas, and Jurij Weinblat. "Default prediction using balance-sheet data: a comparison of models." Journal of Risk Finance 18, no. 5 (2017): 523–40. http://dx.doi.org/10.1108/jrf-01-2017-0003.

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Purpose The purpose of this paper is to do a performance comparison of three different data mining techniques. Design/methodology/approach Logit model, decision tree and random forest are applied in this study on British, French, German, Italian, Portuguese and Spanish balance sheet data from 2006 to 2012, which covers 446,464 firms. Because of the strong imbalance with regard to the solvency status, classification trees and random forests are modified to adapt to this imbalance. All three model specifications are optimized extensively using resampling techniques, relying on the training sampl

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Pearse, Grant D., Michael S. Watt, Julia Soewarto, and Alan Y. S. Tan. "Deep Learning and Phenology Enhance Large-Scale Tree Species Classification in Aerial Imagery during a Biosecurity Response." Remote Sensing 13, no. 9 (2021): 1789. http://dx.doi.org/10.3390/rs13091789.

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The ability of deep convolutional neural networks (deep learning) to learn complex visual characteristics offers a new method to classify tree species using lower-cost data such as regional aerial RGB imagery. In this study, we use 10 cm resolution imagery and 4600 trees to develop a deep learning model to identify Metrosideros excelsa (pōhutukawa)—a culturally important New Zealand tree that displays distinctive red flowers during summer and is under threat from the invasive pathogen Austropuccinia psidii (myrtle rust). Our objectives were to compare the accuracy of deep learning models that

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Lafond, Daniel, Benoît R. Vallières, François Vachon, Marie-Ève St-Louis, and Sébastien Tremblay. "Capturing Non-linear Judgment Policies Using Decision Tree Models of Classification Behavior." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 59, no. 1 (2015): 831–35. http://dx.doi.org/10.1177/1541931215591251.

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Mokarram, Reza, and Mehdi Emadi. "Classification in Non-linear Survival Models Using Cox Regression and Decision Tree." Annals of Data Science 4, no. 3 (2017): 329–40. http://dx.doi.org/10.1007/s40745-017-0105-4.

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Yarak, Kanitta, Apichon Witayangkurn, Kunnaree Kritiyutanont, Chomchanok Arunplod, and Ryosuke Shibasaki. "Oil Palm Tree Detection and Health Classification on High-Resolution Imagery Using Deep Learning." Agriculture 11, no. 2 (2021): 183. http://dx.doi.org/10.3390/agriculture11020183.

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Combining modern technology and agriculture is an important consideration for the effective management of oil palm trees. In this study, an alternative method for oil palm tree management is proposed by applying high-resolution imagery, combined with Faster-RCNN, for automatic detection and health classification of oil palm trees. This study used a total of 4172 bounding boxes of healthy and unhealthy palm trees, constructed from 2000 pixel × 2000 pixel images. Of the total dataset, 90% was used for training and 10% was prepared for testing using Resnet-50 and VGG-16. Three techniques were use

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Hernández, Víctor Adrián Sosa, Raúl Monroy, Miguel Angel Medina-Pérez, Octavio Loyola-González, and Francisco Herrera. "A Practical Tutorial for Decision Tree Induction." ACM Computing Surveys 54, no. 1 (2021): 1–38. http://dx.doi.org/10.1145/3429739.

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Experts from different domains have resorted to machine learning techniques to produce explainable models that support decision-making. Among existing techniques, decision trees have been useful in many application domains for classification. Decision trees can make decisions in a language that is closer to that of the experts. Many researchers have attempted to create better decision tree models by improving the components of the induction algorithm. One of the main components that have been studied and improved is the evaluation measure for candidate splits. In this article, we introduce a t

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Hülsmann, Lisa, Harald Bugmann, and Peter Brang. "How to predict tree death from inventory data — lessons from a systematic assessment of European tree mortality models." Canadian Journal of Forest Research 47, no. 7 (2017): 890–900. http://dx.doi.org/10.1139/cjfr-2016-0224.

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The future development of forest ecosystems depends critically on tree mortality. However, the suitability of empirical mortality algorithms for extrapolation in space or time remains untested. We systematically analyzed the performance of 46 inventory-based mortality models available from the literature using nearly 80 000 independent records from 54 strict forest reserves in Germany and Switzerland covering 11 species. Mortality rates were predicted with higher accuracy if covariates for tree growth and (or) competition at the individual level were included and if models were applied within

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Lima, Nilsa Duarte da Silva, Irenilza de Alencar Nääs, João Gilberto Mendes dos Reis, and Raquel Baracat Tosi Rodrigues da Silva. "Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol." Energies 13, no. 8 (2020): 2067. http://dx.doi.org/10.3390/en13082067.

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The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio). The parameters of the level of energy-environmental efficiency were standardized and categorized according to the Energy-Environmental Efficiency Rating (E-EER). The rating scale varied between lower efficiency (D) and high efficiency + (highest efficiency A+). The classification method with the J48 decision tree and naive Bayes algorithms was used to pre

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Suhaimi Sulaiman, Mohd, and Zuraidi Saad. "Classification of healthy and white root disease infected rubber trees based on relative permittivity and capacitance input properties using LM and SCG artificial neural network." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 1 (2020): 222. http://dx.doi.org/10.11591/ijeecs.v19.i1.pp222-228.

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<span>White root disease is one of the most serious diseases in rubber plantation in Malaysia that originally infects on the root surface of the rubber tree. So, prevention is important compared to treatment. The classification system proposed in the research had the ability of detecting the disease by classifying between healthy rubber trees and white root disease infected rubber trees. 600 samples of latex from healthy rubber trees and white root disease infected rubber trees were taken from the RRIM station in Kota Tinggi, Johor. These samples were measured based on its relative permi

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