Relevant bibliographies by topics / Decision Tree Algorithm

Contents

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

Academic literature on the topic 'Decision Tree Algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 June 2025

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Journal articles on the topic "Decision Tree Algorithm"

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Parlindungan and HariSupriadi. "Implementation Decision Tree Algorithm for Ecommerce Website." International Journal of Psychosocial Rehabilitation 24, no. 02 (2020): 3611–14. http://dx.doi.org/10.37200/ijpr/v24i2/pr200682.

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PURDILA, V., and S. G. PENTIUC. "Fast Decision Tree Algorithm." Advances in Electrical and Computer Engineering 14, no. 1 (2014): 65–68. http://dx.doi.org/10.4316/aece.2014.01010.

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Go, Eunby, Seungmin Lee, and Taeseon Yoon. "Analysis of Ebolavirus with Decision Tree and Apriori algorithm." International Journal of Machine Learning and Computing 4, no. 6 (2014): 543–46. http://dx.doi.org/10.7763/ijmlc.2014.v6.470.

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Kirandeep, Kirandeep, and Prof Neena Madan. "Deployment of ID3 decision tree algorithm for placement prediction." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 740–44. http://dx.doi.org/10.31142/ijtsrd11073.

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Hajjej, Fahima, Manal Abdullah Alohali, Malek Badr, and Md Adnan Rahman. "A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data." BioMed Research International 2022 (July 7, 2022): 1–9. http://dx.doi.org/10.1155/2022/9449497.

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By comparing the performance of various tree algorithms, we can determine which one is most useful for analyzing biomedical data. In artificial intelligence, decision trees are a classification model known for their visual aid in making decisions. WEKA software will evaluate biological data from real patients to see how well the decision tree classification algorithm performs. Another goal of this comparison is to assess whether or not decision trees can serve as an effective tool for medical diagnosis in general. In doing so, we will be able to see which algorithms are the most efficient and appropriate to use when delving into this data and arrive at an informed decision.
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Pratiwi, Reni, Memi Nor Hayati, and Surya Prangga. "PERBANDINGAN KLASIFIKASI ALGORITMA C5.0 DENGAN CLASSIFICATION AND REGRESSION TREE (STUDI KASUS : DATA SOSIAL KEPALA KELUARGA MASYARAKAT DESA TELUK BARU KECAMATAN MUARA ANCALONG TAHUN 2019)." BAREKENG: Jurnal Ilmu Matematika dan Terapan 14, no. 2 (2020): 273–84. http://dx.doi.org/10.30598/barekengvol14iss2pp273-284.

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Decision tree is a algorithm used as a reasoning procedure to get answers from problems are entered. Many methods can be used in decision trees, including the C5.0 algorithm and Classification and Regression Tree (CART). C5.0 algorithm is a non-binary decision tree where the branch of tree can be more than two, while the CART algorithm is a binary decision tree where the branch of tree consists of only two branches. This research aims to determine the classification results of the C5.0 and CART algorithms and to determine the comparison of the accuracy classification results from these two methods. The variables used in this research are the average monthly income (Y), employment (X1), number of family members (X2), last education (X3) and gender (X4). After analyzing the results obtained that the accuracy rate of C5.0 algorithm is 79,17% while the accuracy rate of CART is 84,63%. So it can be said that the CART method is a better method in classifying the average income of the people of Teluk Baru Village in Muara Ancalong District in 2019 compared to the C5.0 algorithm method.
 
 Keywords: C5.0 Algorithm, CART, Classification, Decision Tree.
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Saini, Deepali, and Prof Anand Rajavat. "Performance Evaluation System for Decision Tree Algorithms." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 8 (2013): 2879–86. http://dx.doi.org/10.24297/ijct.v11i8.3006.

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In the machine learning process, classification can be described by supervise learning algorithm. Classification techniques have properties that enable the representation of structures that reflect knowledge of the domain being classified. Industries, education, business and many other domains required knowledge for the growth. Some of the common classification algorithms used in data mining and decision support systems is: Neural networks, Logistic regression, Decision trees etc. The decision regarding most suitable data mining algorithm cannot be made spontaneously. Selection of appropriate data mining algorithm for Business domain required comparative analysis of different algorithms based on several input parameters such as accuracy, build time and memory usage.To make analysis and comparative study, implementation of popular algorithm required on the basis of literature survey and frequency of algorithm used in present scenario. The performance of algorithms are enhanced and evaluated after applying boosting on the trees. We selected numerical and nominal types of dataset and apply on algorithms. Comparative analysis is perform on the result obtain by the system. Then we apply the new dataset in order to generate generate prediction outcome.
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Chandra, B., and P. P. Varghese. "Fuzzy SLIQ Decision Tree Algorithm." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 5 (2008): 1294–301. http://dx.doi.org/10.1109/tsmcb.2008.923529.

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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 parameters like the maximum size of the tree, the minimum number of instances required in a node for inducing a split, node splitting criterion and the amount of pruning. The hyper parameter setting influences the performance of the decision tree model. As researchers, we know that no single setting of hyper parameters works equally well for different datasets. A particular setting that gives an optimal decision tree for one dataset may produce a sub-optimal decision tree model for another dataset. In this paper, we present a hyper heuristic approach for tuning the hyper parameters of Recursive and Partition Trees (rpart), which is a typical implementation of CART in statistical and data analytics package R. We employ an evolutionary algorithm as hyper heuristic for tuning the hyper parameters of the decision tree classifier. The approach is named as Hyper heuristic Evolutionary Approach with Recursive and Partition Trees (HEARpart). The proposed approach is validated on 30 datasets. It is statistically proved that HEARpart performs significantly better than WEKA’s J48 algorithm in terms of error rate, F-measure, and tree size. Further, the suggested hyper heuristic algorithm constructs significantly comprehensible models as compared to WEKA’s J48, CART and other similar decision tree construction strategies. The results show that the accuracy achieved by the hyper heuristic approach is slightly less as compared to the other comparative approaches.
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VUKIĆEVIĆ, MILAN, MILOŠ JOVANOVIĆ, BORIS DELIBAŠIĆ, SONJA IŠLJAMOVIĆ, and MILIJA SUKNOVIĆ. "REUSABLE COMPONENT-BASED ARCHITECTURE FOR DECISION TREE ALGORITHM DESIGN." International Journal on Artificial Intelligence Tools 21, no. 05 (2012): 1250022. http://dx.doi.org/10.1142/s0218213012500224.

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Many decision tree algorithms were proposed over the last few decades. A lack of publishing standards for decision tree algorithm software produced a large time gap between algorithm proposals and their wider application in practice. Non-existence of common repository for storing algorithms and their parts led to a need to re-implement these algorithms from a scratch when they had to be implemented on a different platform. This makes the comparison between algorithms and their partial improvements vague. In addition, combinations and interactions between different algorithm parts haven't been analyzed thoroughly. Reusable component design of decision tree algorithms has been recently suggested as a potential solution to these problems. In this paper we describe an architecture for component-based (white-box) decision tree algorithm design, and we present an open-source framework which enables design and fair testing of decision tree algorithms and their parts. This architecture and developed platform can provide the research community with a common codebase for storing, designing, and evaluating decision tree algorithms (traditional, multivariate and hybrid) and their partial improvements. It is intended for data mining practitioners, algorithm and software developers, and as well for students, as a technology enhanced learning tool.
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Dissertations / Theses on the topic "Decision Tree Algorithm"

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Shi, Haijian. "Best-first Decision Tree Learning." The University of Waikato, 2007. http://hdl.handle.net/10289/2317.

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In best-first top-down induction of decision trees, the best split is added in each step (e.g. the split that maximally reduces the Gini index). This is in contrast to the standard depth-first traversal of a tree. The resulting tree will be the same, just how it is built is different. The objective of this project is to investigate whether it is possible to determine an appropriate tree size on practical datasets by combining best-first decision tree growth with cross-validation-based selection of the number of expansions that are performed. Pre-pruning, post-pruning, CART-pruning can be performed this way to compare.
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Kassim, M. E. "Elliptical cost-sensitive decision tree algorithm (ECSDT)." Thesis, University of Salford, 2018. http://usir.salford.ac.uk/47191/.

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Cost-sensitive multiclass classification problems, in which the task of assessing the impact of the costs associated with different misclassification errors, continues to be one of the major challenging areas for data mining and machine learning. The literature reviews in this area show that most of the cost-sensitive algorithms that have been developed during the last decade were developed to solve binary classification problems where an example from the dataset will be classified into only one of two available classes. Much of the research on cost-sensitive learning has focused on inducing decision trees, which are one of the most common and widely used classification methods, due to the simplicity of constructing them, their transparency and comprehensibility. A review of the literature shows that inducing nonlinear multiclass cost-sensitive decision trees is still in its early stages and further research could result in improvements over the current state of the art. Hence, this research aims to address the following question: 'How can non-linear regions be identified for multiclass problems and utilized to construct decision trees so as to maximize the accuracy of classification, and minimize misclassification costs?' This research addresses this problem by developing a new algorithm called the Elliptical Cost-Sensitive Decision Tree algorithm (ECSDT) that induces cost-sensitive non-linear (elliptical) decision trees for multiclass classification problems using evolutionary optimization methods such as particle swarm optimization (PSO) and Genetic Algorithms (GAs). In this research, ellipses are used as non-linear separators, because of their simplicity and flexibility in drawing non-linear boundaries by modifying and adjusting their size, location and rotation towards achieving optimal results. The new algorithm was developed, tested, and evaluated in three different settings, each with a different objective function. The first considered maximizing the accuracy of classification only; the second focused on minimizing misclassification costs only, while the third considered both accuracy and misclassification cost together. ECSDT was applied to fourteen different binary-class and multiclass data sets and the results have been compared with those obtained by applying some common algorithms from Weka to the same datasets such as J48, NBTree, MetaCost, and the CostSensitiveClassifier. The primary contribution of this research is the development of a new algorithm that shows the benefits of utilizing elliptical boundaries for cost-sensitive decision tree learning. The new algorithm is capable of handling multiclass problems and an empirical evaluation shows good results. More specifically, when considering accuracy only, ECSDT performs better in terms of maximizing accuracy on 10 out of the 14 datasets, and when considering minimizing misclassification costs only, ECSDT performs better on 10 out of the 14 datasets, while when considering both accuracy and misclassification costs, ECSDT was able to obtain higher accuracy on 10 out of the 14 datasets and minimize misclassification costs on 5 out of the 14 datasets. The ECSDT also was able to produce smaller trees when compared with J48, LADTree and ADTree.
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Girardini, Davide <1985&gt. "Efficient implementation of Treant: a robust decision tree learning algorithm." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17423.

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The thesis focuses on the optimization of an existing algorithm called Treant for the generation of robust decision trees. Despite its good performances from the machine learning point of view, unfortunately, the code presented some strong limitations when employed with big datasets. The algorithm was originally written in Python, a very good programming language for fast prototyping but, as well as many other interpreted languages, it can lead to poor performances when it is asked to crunch a big amount of numbers if not supported by appropriated libraries. The code has been translated to the C++ compiled language, it has been parallelized with the OpenMP library, along with other optimizations regarding the memory management and the choice of third party libraries. A python module has been generated from the C++ code in order to expose an interface for the efficient C++ classes and use them as native Python classes. In this way, any python user can exploit both the Python flexibility and the C++ performances.
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Trivedi, Ankit P. "Decision tree-based machine learning algorithm for in-node vehicle classification." Thesis, California State University, Long Beach, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10196455.

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<p> This paper proposes an in-node microprocessor-based vehicle classification approach to analyze and determine the types of vehicles passing over a 3-axis magnetometer sensor. The approach for vehicle classification utilizes J48 classification algorithm implemented in Weka (a machine learning software suite). J48 is Quinlan's C4.5 algorithm, an extension of decision tree machine learning based on an ID3 algorithm. The decision tree model is generated from a set of features extracted from vehicles passing over the 3-axis sensor. The features are attributes provided with correct classifications to the J48 training algorithm to generate a decision tree model with varying degrees of classification rates based on cross-validation. Ideally, using fewer attributes to generate the model allows for the highest computational efficiency due to fewer features needed to be calculated while minimalizing the tree with fewer branches. The generated tree model can then be easily implemented using nested if-loops in any language on a multitude of microprocessors. Also, setting an adaptive baseline to negate the effects of the background magnetic field allows reuse of the same tree model in multiple environments. The result of the experiment shows that the vehicle classification system is effective and efficient.</p>
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Hari, Vijaya. "Empirical Investigation of CART and Decision Tree Extraction from Neural Networks." Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1235676338.

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Krook, Jonatan. "Predicting low airfares with time series features and a decision tree algorithm." Thesis, Uppsala universitet, Statistiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353274.

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Airlines try to maximize revenue by letting prices of tickets vary over time. This fluctuation contains patterns that can be exploited to predict price lows. In this study, we create an algorithm that daily decides whether to buy a certain ticket or wait for the price to go down. For creation and evaluation, we have used data from searches made online for flights on the route Stockholm – New York during 2017 and 2018. The algorithm is based on time series features selected by a decision tree and clearly outperforms the selected benchmarks.
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Feychting, Sara. "Incredible tweets : Automated credibility analysis in Twitter feeds using an alternating decision tree algorithm." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186711.

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This project investigates how to determine the credibility of a tweet without using human perception. Information about the user and the tweet is studied in search for correlations between their properties and the credibility of the tweet. An alternating decision tree is created to automatically determine the credibility of tweets. Some features are found to correlate to the credibility of the tweets, amongst which the number of previous tweets by a user and the use of uppercase characters are the most prominent.
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Gerdes, Mike. "Predictive Health Monitoring for Aircraft Systems using Decision Trees." Licentiate thesis, Linköpings universitet, Fluida och mekatroniska system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-105843.

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Unscheduled aircraft maintenance causes a lot problems and costs for aircraft operators. This is due to the fact that aircraft cause significant costs if flights have to be delayed or canceled and because spares are not always available at any place and sometimes have to be shipped across the world. Reducing the number of unscheduled maintenance is thus a great costs factor for aircraft operators. This thesis describes three methods for aircraft health monitoring and prediction; one method for system monitoring, one method for forecasting of time series and one method that combines the two other methods for one complete monitoring and prediction process. Together the three methods allow the forecasting of possible failures. The two base methods use decision trees for decision making in the processes and genetic optimization to improve the performance of the decision trees and to reduce the need for human interaction. Decision trees have the advantage that the generated code can be fast and easily processed, they can be altered by human experts without much work and they are readable by humans. The human readability and modification of the results is especially important to include special knowledge and to remove errors, which the automated code generation produced.
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Doubleday, Kevin. "Generation of Individualized Treatment Decision Tree Algorithm with Application to Randomized Control Trials and Electronic Medical Record Data." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/613559.

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With new treatments and novel technology available, personalized medicine has become a key topic in the new era of healthcare. Traditional statistical methods for personalized medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials (RCTs). With restricted inclusion and exclusion criteria, data from RCTs may not reflect real world treatment effectiveness. However, electronic medical records (EMR) offers an alternative venue. In this paper, we propose a general framework to identify individualized treatment rule (ITR), which connects the subgroup identification methods and ITR. It is applicable to both RCT and EMR data. Given the large scale of EMR datasets, we develop a recursive partitioning algorithm to solve the problem (ITR-Tree). A variable importance measure is also developed for personalized medicine using random forest. We demonstrate our method through simulations, and apply ITR-Tree to datasets from diabetes studies using both RCT and EMR data. Software package is available at https://github.com/jinjinzhou/ITR.Tree.
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McNamara, Nathan Patrick. "Using Decision Trees to Predict Intent to Use Passive Occupational Exoskeletons in Manufacturing Tasks." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1605720844135027.

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Books on the topic "Decision Tree Algorithm"

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Barros, Rodrigo C., André C. P. L. F. de Carvalho, and Alex A. Freitas. Automatic Design of Decision-Tree Induction Algorithms. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14231-9.

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Z, Hochberg, ed. Practical algorithms in pediatric endocrinology. 2nd ed. Karger, 2007.

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Z, Hochberg, ed. Practical algorithms in pediatric endocrinology. Karger, 1999.

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L, Bready Lois, Noorily Susan H, and Dillman Dawn, eds. Decision making in anesthesiology: An algorithmic approach. 4th ed. Mosby/Elsevier, 2007.

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L, Bready Lois, Dillman Dawn, and Noorily Susan H, eds. Decision making in anesthesiology: An algorithmic approach. 4th ed. Mosby/Elsevier, 2007.

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Shaoul, Ron. Practical algorithms in pediatric gastroenterology. Karger, 2014.

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Khouzam, Nelly. GenID3: A hybrid approach to feature construction in decision trees using genetic algorithms. UMIST, 1997.

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Israel, Zelikovic, and Eisenstein Israel 1964-, eds. Practical algorithms in pediatric nephrology. Karger, 2008.

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ADT 2011 (2011 Piscataway, N.J.). Algorithmic decision theory: Second International Conference, ADT 2011, Piscataway, NJ, USA, October 26-28, 2011 : proceedings. Springer, 2011.

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An Algorithm (decision tree) for the management of Parkinson's Disease: Treatment guidelines. Lippincott-Raven, 1998.

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Book chapters on the topic "Decision Tree Algorithm"

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Yates, Darren, Md Zahidul Islam, and Junbin Gao. "SPAARC: A Fast Decision Tree Algorithm." In Communications in Computer and Information Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6661-1_4.

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Jankowski, Dariusz, and Konrad Jackowski. "Evolutionary Algorithm for Decision Tree Induction." In Computer Information Systems and Industrial Management. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45237-0_4.

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Zhu, Lin, and Yang Yang. "Improvement of Decision Tree ID3 Algorithm." In Collaborate Computing: Networking, Applications and Worksharing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59288-6_59.

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Manjula, R., and R. Anitha. "Identification of Encryption Algorithm Using Decision Tree." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17881-8_23.

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Islam, Md Zahidul. "EXPLORE: A Novel Decision Tree Classification Algorithm." In Data Security and Security Data. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25704-9_7.

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Kim, Myung Won, and Joung Woo Ryu. "Optimized Fuzzy Decision Tree Using Genetic Algorithm." In Neural Information Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893295_88.

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Salem, Abdel-Badeeh M., and Abeer M. Mahmoud. "A Hybrid Genetic Algorithm — Decision Tree Classifier." In Intelligent Information Processing and Web Mining. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36562-4_23.

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Potharst, Rob, and Jan C. Bioch. "A Decision Tree Algorithm for Ordinal Classification." In Advances in Intelligent Data Analysis. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48412-4_16.

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Kura, Satoshi, Hiroshi Unno, and Ichiro Hasuo. "Decision Tree Learning in CEGIS-Based Termination Analysis." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_4.

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AbstractWe present a novel decision tree-based synthesis algorithm of ranking functions for verifying program termination. Our algorithm is integrated into the workflow of CounterExample Guided Inductive Synthesis (CEGIS). CEGIS is an iterative learning model where, at each iteration, (1) a synthesizer synthesizes a candidate solution from the current examples, and (2) a validator accepts the candidate solution if it is correct, or rejects it providing counterexamples as part of the next examples. Our main novelty is in the design of a synthesizer: building on top of a usual decision tree learning algorithm, our algorithm detects cycles in a set of example transitions and uses them for refining decision trees. We have implemented the proposed method and obtained promising experimental results on existing benchmark sets of (non-)termination verification problems that require synthesis of piecewise-defined lexicographic affine ranking functions.
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Wang, Lei. "Optimal Decision Programming of Decision Tree Algorithm in Project Management." In Cyber Security Intelligence and Analytics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97874-7_117.

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Conference papers on the topic "Decision Tree Algorithm"

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Meng, Qing-wu, Qiang He, Ning Li, Xiang-ran Du, and Li-na Su. "Crisp Decision Tree Induction Based on Fuzzy Decision Tree Algorithm." In 2009 First International Conference on Information Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/icise.2009.440.

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Xudong, Song, and Cheng Xiaolan. "Decision Tree Algorithm based on Sampling." In 2007 IFIP International Conference on Network and Parallel Computing Workshops (NPC 2007). IEEE, 2007. http://dx.doi.org/10.1109/npc.2007.133.

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Qingyun, Chi. "Research on incremental decision tree algorithm." In Mechanical Engineering and Information Technology (EMEIT). IEEE, 2011. http://dx.doi.org/10.1109/emeit.2011.6022930.

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Chen Jin, Luo De-lin, and Mu Fen-xiang. "An improved ID3 decision tree algorithm." In Education (ICCSE). IEEE, 2009. http://dx.doi.org/10.1109/iccse.2009.5228509.

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Xudong, Song, and Cheng Xiaolan. "Decision Tree Algorithm based on Sampling." In 2007 IFIP International Conference on Network and Parallel Computing Workshops (NPC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icnpcw.2007.4351564.

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Lu, Yifan, Tianle Ye, and Jiali Zheng. "Decision Tree Algorithm in Machine Learning." In 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA). IEEE, 2022. http://dx.doi.org/10.1109/aeeca55500.2022.9918857.

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Liu, Run Zong, Yuan Yan Tang, and Bin Fang. "Automatic decision support by information energy decision tree algorithm." In 2014 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2014. http://dx.doi.org/10.1109/smc.2014.6974566.

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Liu, Run-Zong, Bin Fang, and Hui-Wu Luo. "Automatic decision support by rule exhaustion decision tree algorithm." In 2016 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2016. http://dx.doi.org/10.1109/icwapr.2016.7731623.

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Zoulkarni, Asim, Christoforos Kachris, and Dimitrios Soudris. "Hardware Acceleration of Decision Tree Learning Algorithm." In 2020 9th International Conference on Modern Circuits and Systems Technologies (MOCAST). IEEE, 2020. http://dx.doi.org/10.1109/mocast49295.2020.9200255.

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Wenlong, Li, and Xing Changzheng. "Parallel Decision Tree Algorithm Based on Combination." In 2010 International Forum on Information Technology and Applications (IFITA). IEEE, 2010. http://dx.doi.org/10.1109/ifita.2010.115.

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Reports on the topic "Decision Tree Algorithm"

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Lorenz, Markus. Auswirkungen des Decoy-Effekts auf die Algorithm Aversion. Sonderforschungsgruppe Institutionenanalyse, 2022. http://dx.doi.org/10.46850/sofia.9783947850013.

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Limitations in the human decision-making process restrict the technological potential of algorithms, which is also referred to as "algorithm aversion". This study uses a laboratory experiment with participants to investigate whether a phenomenon known since 1982 as the "decoy effect" is suitable for reducing algorithm aversion. For numerous analogue products, such as cars, drinks or newspaper subscriptions, the Decoy Effect is known to have a strong influence on human decision-making behaviour. Surprisingly, the decisions between forecasts by humans and Robo Advisors (algorithms) investigated in this study are not influenced by the Decoy Effect at all. This is true both a priori and after observing forecast errors.
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Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.
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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 major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved.
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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 samples of model input. The first dataset consisted of transmission loss (TL) fields for single realizations of turbulence. The second dataset consisted of average TL fields for 64 realizations of turbulence. Three machine-learning algorithms were applied to each dataset, namely, ensemble decision trees, neural networks, and cluster-weighted models. Observational data come from a long-range (out to 8 km) sound propagation experiment. In comparison to the experimental observations, regression predictions have 5–7 dB in median absolute error. Surrogate data quality depends on an accurate characterization of refractive and scattering conditions. Predictions obtained through a single realization of turbulence agree better with the experimental observations.
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Enhancing quality for clients: The balanced counseling strategy. Population Council, 2003. http://dx.doi.org/10.31899/rh2003.1014.

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A central focus of high-quality family-planning care is the interaction between clients and the providers who serve them. In the ideal client-provider interaction, the provider treats all clients respectfully, responds to their reproductive needs and intentions, helps in the selection of the most appropriate family planning method, and offers sufficient information to use the method safely and effectively. To improve the quality of the client-provider interaction, Population Council staff developed a “Balanced Counseling Strategy,” a type of algorithm or decision tree, to be used in combination with several job aids, or visual memory aids. The Balanced Counseling Strategy structures the client-provider interaction to focus on the client’s needs and support the client’s choice of an appropriate method, and leads to improvements in the client-provider interaction when providers use the strategy along with job aids. This brief describes the Balanced Counseling Strategy as an ongoing approach to improving quality of care. It outlines the origin and rationale for developing the strategy and details its subsequent adaptation for use in other contexts.
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