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Опубліковано: 14 березня 2023

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Статті в журналах з теми "Decision Tree with CART algorithm"

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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 (September 7, 2020): 273–84. http://dx.doi.org/10.30598/barekengvol14iss2pp273-284.

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Анотація:
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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Okada, Hugo Kenji Rodrigues, Andre Ricardo Nascimento das Neves, and Ricardo Shitsuka. "Analysis of Decision Tree Induction Algorithms." Research, Society and Development 8, no. 11 (August 24, 2019): e298111473. http://dx.doi.org/10.33448/rsd-v8i11.1473.

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Анотація:
Decision trees are data structures or computational methods that enable nonparametric supervised machine learning and are used in classification and regression tasks. The aim of this paper is to present a comparison between the decision tree induction algorithms C4.5 and CART. A quantitative study is performed in which the two methods are compared by analyzing the following aspects: operation and complexity. The experiments presented practically equal hit percentages in the execution time for tree induction, however, the CART algorithm was approximately 46.24% slower than C4.5 and was considered to be more effective.
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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 (February 21, 2021): 249–76. http://dx.doi.org/10.32890/jict2021.20.2.5.

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Анотація:
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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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 (September 2003): 207–25. http://dx.doi.org/10.1142/s0218213003001204.

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Анотація:
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 using the SPRINT decision tree algorithm is presented. Many classification algorithms have memory limitations including the requirement that datasets be memory resident. SPRINT removes all of these limitations and provides a fast and scalable analysis. It is an extension of a commonly used decision tree algorithm, CART, and provides a unique tree pruning technique based on the Minimum Description Length (MDL) principle. Combining the MDL pruning technique and the modified classification algorithm, SPRINT yields classification trees with useful accuracy. The case study used consists of software metrics collected from a very large telecommunications system. It is observed that classification trees built by SPRINT are more balanced and demonstrate better stability than those built by CART.
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Duan, Huajie, Zhengdong Deng, Feifan Deng, and Daqing Wang. "Assessment of Groundwater Potential Based on Multicriteria Decision Making Model and Decision Tree Algorithms." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/2064575.

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Анотація:
Groundwater plays an important role in global climate change and satisfying human needs. In the study, RS (remote sensing) and GIS (geographic information system) were utilized to generate five thematic layers, lithology, lineament density, topology, slope, and river density considered as factors influencing the groundwater potential. Then, the multicriteria decision model (MCDM) was integrated with C5.0 and CART, respectively, to generate the decision tree with 80 surveyed tube wells divided into four classes on the basis of the yield. To test the precision of the decision tree algorithms, the 10-fold cross validation and kappa coefficient were adopted and the average kappa coefficient for C5.0 and CART was 90.45% and 85.09%, respectively. After applying the decision tree to the whole study area, four classes of groundwater potential zones were demarcated. According to the classification result, the four grades of groundwater potential zones, “very good,” “good,” “moderate,” and “poor,” occupy 4.61%, 8.58%, 26.59%, and 60.23%, respectively, with C5.0 algorithm, while occupying the percentages of 4.68%, 10.09%, 26.10%, and 59.13%, respectively, with CART algorithm. Therefore, we can draw the conclusion that C5.0 algorithm is more appropriate than CART for the groundwater potential zone prediction.
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Yu, Shuang, Xiongfei Li, Hancheng Wang, Xiaoli Zhang, and Shiping Chen. "C_CART: An instance confidence-based decision tree algorithm for classification." Intelligent Data Analysis 25, no. 4 (July 9, 2021): 929–48. http://dx.doi.org/10.3233/ida-205361.

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Анотація:
In classification, a decision tree is a common model due to its simple structure and easy understanding. Most of decision tree algorithms assume all instances in a dataset have the same degree of confidence, so they use the same generation and pruning strategies for all training instances. In fact, the instances with greater degree of confidence are more useful than the ones with lower degree of confidence in the same dataset. Therefore, the instances should be treated discriminately according to their corresponding confidence degrees when training classifiers. In this paper, we investigate the impact and significance of degree of confidence of instances on the classification performance of decision tree algorithms, taking the classification and regression tree (CART) algorithm as an example. First, the degree of confidence of instances is quantified from a statistical perspective. Then, a developed CART algorithm named C_CART is proposed by introducing the confidence of instances into the generation and pruning processes of CART algorithm. Finally, we conduct experiments to evaluate the performance of C_CART algorithm. The experimental results show that our C_CART algorithm can significantly improve the generalization performance as well as avoiding the over-fitting problem to a certain extend.
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Barros, Rodrigo C., Márcio P. Basgalupp, André C. P. L. F. de Carvalho, and Alex A. Freitas. "Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms." Evolutionary Computation 21, no. 4 (November 2013): 659–84. http://dx.doi.org/10.1162/evco_a_00101.

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Анотація:
This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. The algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART.
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Jun, Sungbum. "Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing." Sensors 21, no. 8 (April 18, 2021): 2849. http://dx.doi.org/10.3390/s21082849.

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Анотація:
Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniques, such as tree-based algorithms, have drawn attention to the need to implement reliable manufacturing systems, and identify the root causes of faults. However, despite the high interpretability of decision trees, tree-based models make a trade-off between accuracy and interpretability. In order to improve the tree’s performance while maintaining its interpretability, an evolutionary algorithm for discretization of multiple attributes, called Decision tree Improved by Multiple sPLits with Evolutionary algorithm for Discretization (DIMPLED), is proposed. The experimental results with two real-world datasets from sensors showed that the decision tree improved by DIMPLED outperformed the performances of single-decision-tree models (C4.5 and CART) that are widely used in practice, and it proved competitive compared to the ensemble methods, which have multiple decision trees. Even though the ensemble methods could produce slightly better performances, the proposed DIMPLED has a more interpretable structure, while maintaining an appropriate performance level.
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Liu, Biao, and Zhipeng Sun. "Global Economic Market Forecast and Decision System for IoT and Machine Learning." Mobile Information Systems 2022 (April 20, 2022): 1–12. http://dx.doi.org/10.1155/2022/8344791.

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Анотація:
The fast growth of IoT in wearable devices, smart sensors, and home appliances will affect every aspect of our lives. With the rapid development of economic globalization, how to integrate science and technology into economic decision-making is the focus of the current research field, and the research of this paper is precisely to solve this problem. This paper proposes a global economic market forecasting and decision-making system research based on the Internet of Things and machine learning. Using the wireless sensor network of the Internet of Things technology to perceive and predict the global economic market, through the decision tree method in machine learning, and combine the global economic market to make economic decisions, this paper explores the decision tree algorithm with the highest execution efficiency through the experimental comparison of four decision tree algorithms: ID3 algorithm, C4.5 algorithm, CART algorithm, and IQ algorithm. The output of the experiments in the paper indicates that the C4.5 algorithm has the fastest running speed. When the dataset increases to 110,000, its running time reaches 503 s.
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Yang, Bao Hua, and Shuang Li. "Remote Sense Image Classification Based on CART Algorithm." Advanced Materials Research 864-867 (December 2013): 2782–86. http://dx.doi.org/10.4028/www.scientific.net/amr.864-867.2782.

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Анотація:
This papers deals with the study of the algorithm of classification method based on decision tree for remote sensing image. The experimental area is located in the Xiangyang district, the data source for the 2010 satellite images of SPOT and TM fusion. Moreover, classification method based on decision tree is optimized with the help of the module of RuleGen and applied in regional remote sensing image of interest. The precision of Maximum likelihood ratio is 95.15 percent, and 94.82 percent for CRAT. Experimental results show that the classification method based on classification and regression tree method is as well as the traditional one.
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Більше джерел

Дисертації з теми "Decision Tree with CART algorithm"

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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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Konda, Ramesh. "Predicting Machining Rate in Non-Traditional Machining using Decision Tree Inductive Learning." NSUWorks, 2010. http://nsuworks.nova.edu/gscis_etd/199.

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Wire Electrical Discharge Machining (WEDM) is a nontraditional machining process used for machining intricate shapes in high strength and temperature resistive (HSTR) materials. WEDM provides high accuracy, repeatability, and a better surface finish; however the tradeoff is a very slow machining rate. Due to the slow machining rate in WEDM, machining tasks take many hours depending on the complexity of the job. Because of this, users of WEDM try to predict machining rate beforehand so that input parameter values can be pre-programmed to achieve automated machining. However, partial success with traditional methodologies such as thermal modeling, artificial neural networks, mathematical, statistical, and empirical models left this problem still open for further research and exploration of alternative methods. Also, earlier efforts in applying the decision tree rule induction algorithms for predicting the machining rate in WEDM had limitations such as use of coarse grained method of discretizing the target and exploration of only C4.5 as the learning algorithm. The goal of this dissertation was to address the limitations reported in literature in using decision tree rule induction algorithms for WEDM. In this study, the three decision tree inductive algorithms C5.0, CART and CHAID have been applied for predicting material removal rate when the target was discretized into varied number of classes (two, three, four, and five classes) by three discretization methods. There were a total of 36 distinct combinations when learning algorithms, discretization methods, and number of classes in the target are combined. All of these 36 models have been developed and evaluated based on the prediction accuracy. From this research, a total of 21 models found to be suitable for WEDM that have prediction accuracy ranging from 71.43% through 100%. The models indentified in the current study not only achieved better prediction accuracy compared to previous studies, but also allows the users to have much better control over WEDM than what was previously possible. Application of inductive learning and development of suitable predictive models for WEDM by incorporating varied number of classes in the target, different learning algorithms, and different discretization methods have been the major contribution of this research.
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3

Fernandes, Fabiano Rodrigues. "Emprego de diferentes algoritmos de árvores de decisão na classificação da atividade celular in vitro para tratamentos de superfícies de titânio." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/165456.

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Анотація:
O interesse pela área de análise e caracterização de materiais biomédicos cresce, devido a necessidade de selecionar de forma adequada, o material a ser utilizado. Dependendo das condições em que o material será submetido, a caracterização poderá abranger a avaliação de propriedades mecânicas, elétricas, bioatividade, imunogenicidade, eletrônicas, magnéticas, ópticas, químicas e térmicas. A literatura relata o emprego da técnica de árvores de decisão, utilizando os algoritmos SimpleCart(CART) e J48, para classificação de base de dados (dataset), gerada a partir de resultados de artigos científicos. Esse estudo foi realizado afim de identificar características superficiais que otimizassem a atividade celular. Para isso, avaliou-se, a partir de artigos publicados, o efeito de tratamento de superfície do titânio na atividade celular in vitro (células MC3TE-E1). Ficou constatado que, o emprego do algoritmo SimpleCart proporcionou uma melhor resposta em relação ao algoritmo J48. Nesse contexto, o presente trabalho tem como objetivo aplicar, para esse mesmo estudo, os algoritmos CHAID (Chi-square iteration automatic detection) e CHAID Exaustivo, comparando com os resultados obtidos com o emprego do algoritmo SimpleCart. A validação dos resultados, mostraram que o algoritmo CHAID Exaustivo obteve o melhor resultado em comparação ao algoritmo CHAID, obtendo uma estimativa de acerto de 75,9% contra 58,6% respectivamente, e um erro padrão de 7,9% contra 9,1% respectivamente, enquanto que, o algoritmo já testado na literatura SimpleCart(CART) teve como resultado 34,5% de estimativa de acerto com um erro padrão de 8,8%. Com relação aos tempos de execução apurados sobre 22 mil registros, evidenciaram que o algoritmo CHAID Exaustivo apresentou os melhores tempos, com ganho de 0,02 segundos sobre o algoritmo CHAID e 14,45 segundos sobre o algoritmo SimpleCart(CART).
The interest for the area of analysis and characterization of biomedical materials as the need for selecting the adequate material to be used increases. However, depending on the conditions to which materials are submitted, characterization may involve the evaluation of mechanical, electrical, optical, chemical and thermal properties besides bioactivity and immunogenicity. Literature review shows the application decision trees, using SimpleCart(CART) and J48 algorithms, to classify the dataset, which is generated from the results of scientific articles. Therefore the objective of this study was to identify surface characteristics that optimizes the cellular activity. Based on published articles, the effect of the surface treatment of titanium on the in vitro cells (MC3TE-E1 cells) was evaluated. It was found that applying SimpleCart algorithm gives better results than the J48. In this sense, the present study has the objective to apply the CHAID (Chi-square iteration automatic detection) algorithm and Exhaustive CHAID to the surveyed data, and compare the results obtained with the application of SimpleCart algorithm. The validation of the results showed that the Exhaustive CHAID obtained better results comparing to CHAID algorithm, obtaining 75.9 % of accurate estimation against 58.5%, respectively, while the standard error was 7.9% against 9.1%, respectively. Comparing the obtained results with SimpleCart(CART) results which had already been tested and presented in the literature, the results for accurate estimation was 34.5% and the standard error 8.8%. In relation to execution time found through the 22.000 registers, it showed that the algorithm Exhaustive CHAID presented the best times, with a gain of 0.02 seconds over the CHAID algorithm and 14.45 seconds over the SimpleCart(CART) algorithm.
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4

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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Анотація:
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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5

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

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Анотація:
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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6

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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Анотація:
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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7

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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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.

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8

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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Анотація:
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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9

Jeenanunta, Chawalit. "The Approach-dependent, Time-dependent, Label-constrained Shortest Path Problem and Enhancements for the CART Algorithm with Application to Transportation Systems." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/27773.

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Анотація:
In this dissertation, we consider two important problems pertaining to the analysis of transportation systems. The first of these is an approach-dependent, time-dependent, label-constrained shortest path problem that arises in the context of the Route Planner Module of the Transportation Analysis Simulation System (TRANSIMS), which has been developed by the Los Alamos National Laboratory for the Federal Highway Administration. This is a variant of the shortest path problem defined on a transportation network comprised of a set of nodes and a set of directed arcs such that each arc has an associated label designating a mode of transportation, and an associated travel time function that depends on the time of arrival at the tail node, as well as on the node via which this node was approached. The lattermost feature is a new concept injected into the time-dependent, label-constrained shortest path problem, and is used to model turn-penalties in transportation networks. The time spent at an intersection before entering the next link would depend on whether we travel straight through the intersection, or make a right turn at it, or make a left turn at it. Accordingly, we model this situation by incorporating within each link's travel time function a dependence on the link via which its tail node was approached. We propose two effective algorithms to solve this problem by adapting two efficient existing algorithms to handle time dependency and label constraints: the Partitioned Shortest Path (PSP) algorithm and the Heap-Dijkstra (HP-Dijkstra) algorithm, and present related theoretical complexity results. In addition, we also explore various heuristic methods to curtail the search. We explore an Augmented Ellipsoidal Region Technique (A-ERT) and a Distance-Based A-ERT, along with some variants to curtail the search for an optimal path between a given origin and destination to more promising subsets of the network. This helps speed up computation without sacrificing optimality. We also incorporate an approach-dependent delay estimation function, and in concert with a search tree level-based technique, we derive a total estimated travel time and use this as a key to prioritize node selections or to sort elements in the heap. As soon as we reach the destination node, while it is within some p% of the minimum key value of the heap, we then terminate the search. We name the versions of PSP and HP-Dijkstra that employ this method as Early Terminated PSP (ET-PSP) and Early Terminated Heap-Dijkstra (ETHP-Dijkstra) algorithms. All of these procedures are compared with the original Route Planner Module within TRANSIMS, which is implemented in the Linux operating system, using C++ along with the g++ GNU compiler. Extensive computational testing has been conducted using available data from the Portland, Oregon, and Blacksburg, Virginia, transportation networks to investigate the efficacy of the developed procedures. In particular, we have tested twenty-five different combinations of network curtailment and algorithmic strategies on three test networks: the Blacksburg-light, the Blacksburg-full, and the BigNet network. The results indicate that the Heap-Dijkstra algorithm implementations are much faster than the PSP algorithmic approaches for solving the underlying problem exactly. Furthermore, mong the curtailment schemes, the ETHP-Dijkstra with p=5%, yields the best overall results. This method produces solutions within 0.37-1.91% of optimality, while decreasing CPU effort by 56.68% at an average, as compared with applying the best available exact algorithm. The second part of this dissertation is concerned with the Classification and Regression Tree (CART) algorithm, and its application to the Activity Generation Module of TRANSIMS. The CART algorithm has been popularly used in various contexts by transportation engineers and planners to correlate a set of independent household demographic variables with certain dependent activity or travel time variables. However, the algorithm lacks an automated mechanism for deriving classification trees based on optimizing specified objective functions and handling desired side-constraints that govern the structure of the tree and the statistical and demographic nature of its leaf nodes. Using a novel set partitioning formulation, we propose new tree development, and more importantly, optimal pruning strategies to accommodate the consideration of such objective functions and side-constraints, and establish the theoretical validity of our approach. This general enhancement of the CART algorithm is then applied to the Activity Generator module of TRANSIMS. Related computational results are presented using real data pertaining to the Portland, Oregon, and Blacksburg, Virginia, transportation networks to demonstrate the flexibility and effectiveness of the proposed approach in classifying data, as well as to examine its numerical performance. The results indicate that a variety of objective functions and constraints can be readily accommodated to efficiently control the structural information that is captured by the developed classification tree as desired by the planner or analyst, dependent on the scope of the application at hand.
Ph. D.
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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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Книги з теми "Decision Tree with CART algorithm"

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

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Decision Making in Anesthesiology: An Algorithmic Approach (Decision Making). 3rd ed. Mosby, 1999.

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Bready, Lois L., Susan Helene Noorily, and Dawn Dillman. Decision Making in Anesthesiology. 4th ed. Mosby, 2007.

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

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Kulak, Dariusz. Wieloaspektowa metoda oceny stanu gleb leśnych po przeprowadzeniu procesów pozyskania drewna. Publishing House of the University of Agriculture in Krakow, 2017. http://dx.doi.org/10.15576/978-83-66602-28-1.

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Presented reasearch aimed to develop and analyse the suitability of the CART models for prediction of the extent and probability of occurrence of damage to outer soil layers caused by timber harvesting performed under varied conditions. Having employed these models, the author identified certain methods of logging works and conditions, under which they should be performed to minimise the risk of damaging forest soils. The analyses presented in this work covered the condition of soils upon completion of logging works, which was investigated in 48 stands located in central and south-eastern Poland. In the stands selected for these studies a few felling treatments were carried out, including early thinning, late thinning and final felling. Logging works were performed with use of the most popular technologies in Poland. Trees were cut down with chainsaws and timber was extracted by means of various skidding methods: with horses, semi-suspended skidding with the use of cable yarding systems, farm tractors equipped with cable winches or tractors of a skidder type, and forwarding employing farm tractors with trailers loaded mechanically by cranes or manually. The analyses also included mechanised forest operation with the use of a harvester and a forwarder. The information about the extent of damage to soil, in a form of wheel-ruts and furrows, gathered in the course of soil condition inventory served for construction of regression tree models using the CART method (Classification and Regression Trees), based on which the area, depth and the volume of soil damage under analysis, wheel-ruts and furrows, were determined, and the total degree of all soil disturbances was assessed. The CART classification trees were used for modelling the probability of occurrence of wheel-ruts and furrows, or any other type of soil damage. Qualitative independent variables assumed by the author for developing the models included several characteristics describing the conditions under which the logging works were performed, mensuration data of the stands and the treatments conducted there. These characteristics covered in particular: the season of the year when logging works were performed, the system of timber harvesting employed, the manner of timber skidding, the means engaged in the process of timber harvesting and skidding, habitat type, crown closure, and cutting category. Moreover, the author took into consideration an impact of the quantitative independent variables on the extent and probability of occurrence of soil disturbance. These variables included the following: the measuring row number specifying a distance between the particular soil damage and communication tracks, the age of a stand, the soil moisture content, the intensity of a particular cutting treatment expressed by units of harvested timber volume per one hectare of the stand, and the mean angle of terrain inclination. The CART models developed in these studies not only allowed the author to identify the conditions, under which the soil damage of a given degree is most likely to emerge, or determine the probability of its occurrence, but also, thanks to a graphical presentation of the nature and strength of relationships between the variables employed in the model construction, they facilitated a recognition of rules and relationships between these variables and the area, depth, volume and probability of occurrence of forest soil damage of a particular type. Moreover, the CART trees served for developing the so-called decision-making rules, which are especially useful in organising logging works. These rules allow the organisers of timber harvest to plan the management-related actions and operations with the use of available technical means and under conditions enabling their execution in such manner as to minimise the harm to forest soils. Furthermore, employing the CART trees for modelling soil disturbance made it possible to evaluate particular independent variables in terms of their impact on the values of dependent variables describing the recorded disturbance to outer soil layers. Thanks to this the author was able to identify, amongst the variables used in modelling the properties of soil damage, these particular ones that had the greatest impact on values of these properties, and determine the strength of this impact. Detailed results depended on the form of soil disturbance and the particular characteristics subject to analysis, however the variables with the strongest influence on the extent and probability of occurrence of soil damage, under the conditions encountered in the investigated stands, enclosed the following: the season of the year when logging works were performed, the volume-based cutting intensity of the felling treatments conducted, technical means used for completion of logging works, the soil moisture content during timber harvest, the manner of timber skidding, dragged, semi-suspended or forwarding, and finally a distance between the soil damage and transportation ducts. The CART models proved to be very useful in designing timber harvesting technologies that could minimise the risk of forest soil damage in terms of both, the extent of factual disturbance and the probability of its occurrence. Another valuable advantage of this kind of modelling is an opportunity to evaluate an impact of particular variables on the extent and probability of occurrence of damage to outer soil layers. This allows the investigator to identify, amongst all of the variables describing timber harvesting processes, those crucial ones, from which any optimisation process should start, in order to minimise the negative impact of forest management practices on soil condition.
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Частини книг з теми "Decision Tree with CART algorithm"

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Javed Mehedi Shamrat, F. M., Rumesh Ranjan, Khan Md Hasib, Amit Yadav, and Abdul Hasib Siddique. "Performance Evaluation Among ID3, C4.5, and CART Decision Tree Algorithm." In Pervasive Computing and Social Networking, 127–42. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5640-8_11.

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Liu, Haotian, Jiangfeng Jin, Kun Liu, Jiaping Zhang, and Yanan Niu. "Research on UAV Air Combat Maneuver Decision Based on Decision Tree CART Algorithm." In Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022), 2638–50. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0479-2_243.

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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, 43–55. Singapore: 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, 23–32. Berlin, Heidelberg: 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, 595–600. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59288-6_59.

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Mahmood, Ali Mirza, Mohammad Imran, Naganjaneyulu Satuluri, Mrithyumjaya Rao Kuppa, and Vemulakonda Rajesh. "An Improved CART Decision Tree for Datasets with Irrelevant Feature." In Swarm, Evolutionary, and Memetic Computing, 539–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27172-4_64.

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Manjula, R., and R. Anitha. "Identification of Encryption Algorithm Using Decision Tree." In Communications in Computer and Information Science, 237–46. Berlin, Heidelberg: 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, 55–71. Berlin, Heidelberg: 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, 797–806. Berlin, Heidelberg: 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, 221–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36562-4_23.

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Тези доповідей конференцій з теми "Decision Tree with CART algorithm"

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Aziza, Elaouaber Zineb, Lazouni Mohamed El Amine, Messadi Mohamed, and Bessaid Abdelhafid. "Decision tree CART algorithm for diabetic retinopathy classification." In 2019 6th International Conference on Image and Signal Processing and their Applications (ISPA). IEEE, 2019. http://dx.doi.org/10.1109/ispa48434.2019.8966905.

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Xie, Tiantian, Runchuan Li, Xingjin Zhang, Bing Zhou, and Zongmin Wang. "Research on Heartbeat Classification Algorithm Based on CART Decision Tree." In 2019 8th International Symposium on Next Generation Electronics (ISNE). IEEE, 2019. http://dx.doi.org/10.1109/isne.2019.8896650.

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Ma, RongFei, Wenxia Xu, Baocheng Yu, Min Zhang, Jing Wu, and Huizhi Zhu. "CART Decision Tree Based Human State Estimation Algorithm and Research." In 2022 4th International Conference on Robotics and Computer Vision (ICRCV). IEEE, 2022. http://dx.doi.org/10.1109/icrcv55858.2022.9953220.

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Li, Miao. "Application of CART decision tree combined with PCA algorithm in intrusion detection." In 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2017. http://dx.doi.org/10.1109/icsess.2017.8342859.

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Ersoy, Elif, Erinç Albey, and Enis Kayış. "A CART-based Genetic Algorithm for Constructing Higher Accuracy Decision Trees." In 9th International Conference on Data Science, Technology and Applications. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009893903280338.

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Tan, Huaxing, and Ke Zhao. "Application of Iterative CART Decision Tree Algorithm in Studying Influence of Early Education Curriculum on Children’s Attention Improvement." In 2022 2nd International Conference on Social Sciences and Intelligence Management (SSIM). IEEE, 2022. http://dx.doi.org/10.1109/ssim55504.2022.10047947.

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Idogun, Akpevwe Kelvin, Ruth Oyanu Ujah, and Lesley Anne James. "Surrogate-Based Analysis of Chemical Enhanced Oil Recovery – A Comparative Analysis of Machine Learning Model Performance." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/208452-ms.

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Abstract Optimizing decision and design variables for Chemical EOR is imperative for sensitivity and uncertainty analysis. However, these processes involve multiple reservoir simulation runs which increase computational cost and time. Surrogate models are capable of overcoming this impediment as they are capable of mimicking the capabilities of full field three-dimensional reservoir simulation models in detail and complexity. Artificial Neural Networks (ANN) and regression-based Design of Experiments (DoE) are common methods for surrogate modelling. In this study, a comparative analysis of data-driven surrogate model performance on Recovery Factor (RF) for Surfactant-Polymer flooding is investigated with seven input variables including Kv/Kh ratio, polymer concentration in polymer drive, surfactant slug size, surfactant concentration in surfactant slug, polymer concentration in surfactant slug, polymer drive size and salinity of polymer drive. Eleven Machine learning models including Multiple Linear Regression (MLR), Ridge and Lasso regression; Support Vector Regression (SVR), ANN as well as Classification and Regression Tree (CART) based algorithms including Decision Trees, Random Forest, eXtreme Gradient Boosting (XGBoost), Gradient Boosting and Extremely Randomized Trees (ERT), are applied on a dataset consisting of 202 datapoints. The results obtained indicate high model performance and accuracy for SVR, ANN and CART based ensemble techniques like Extremely Randomized Trees, Gradient Boost and XGBoost regression, with high R2 values and lowest Mean Squared Error (MSE) values for the training and test dataset. Unlike other studies on Chemical EOR surrogate modelling where sensitivity was analyzed with statistical DoE, we rank the input features using Decision Tree-based algorithms while model interpretability is achieved with Shapely Values. Results from feature ranking indicate that surfactant concentration, and slug size are the most influential parameters on the RF. Other important factors, though with less influence, are the polymer concentration in surfactant slug, polymer concentration in polymer drive and polymer drive size. The salinity of the polymer drive and the Kv/Kh ratio both have a negative effect on the RF, with a corresponding least level of significance.
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Myint, Khin, and Hlaing Htake Khaung Tin. "Analyzing the Comparison of C4.5, CART and C5.0 Algorithms on Heart Disease Dataset using Decision Tree Method." In Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 February 2020, Jamia Hamdard, New Delhi, India. EAI, 2021. http://dx.doi.org/10.4108/eai.27-2-2020.2303221.

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Syafrudin, Muhammad, Ganjar Alfian, Norma Latif Fitriyani, Abdul Hafidh Sidiq, Tjahjanto Tjahjanto, and Jongtae Rhee. "Improving Efficiency of Self-care Classification Using PCA and Decision Tree Algorithm." In 2020 International Conference on Decision Aid Sciences and Application (DASA). IEEE, 2020. http://dx.doi.org/10.1109/dasa51403.2020.9317243.

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Wati, Masna, Heliza Rahmania Hatta, Ayunda Dwi Saputri, Anindita Septiarini, and Muh Jamil. "Implementation of the C4.5 Decision Tree Algorithm Method for Selection of Facial Mask Skin Care Products." In 2022 5th International Conference on Information and Communications Technology (ICOIACT). IEEE, 2022. http://dx.doi.org/10.1109/icoiact55506.2022.9972225.

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Звіти організацій з теми "Decision Tree with CART 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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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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