Academic literature on the topic 'Decision Tree with CART algorithm'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Decision Tree with CART algorithm.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Decision Tree with CART algorithm"

1

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.

Full text
Abstract:
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 met
APA, Harvard, Vancouver, ISO, and other styles
2

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 (2019): e298111473. http://dx.doi.org/10.33448/rsd-v8i11.1473.

Full text
Abstract:
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 consider
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
Abstract:
Decision tree models have earned a special status in predictive modeling since these are considered comprehensible for human analysis and insight. Classification and Regression Tree (CART) algorithm is one of the renowned decision tree induction algorithms to address the classification as well as regression problems. Finding optimal values for the hyper parameters of a decision tree construction algorithm is a challenging issue. While making an effective decision tree classifier with high accuracy and comprehensibility, we need to address the question of setting optimal values for its hyper pa
APA, Harvard, Vancouver, ISO, and other styles
4

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

Full text
Abstract:
Predicting the quality of system modules prior to software testing and operations can benefit the software development team. Such a timely reliability estimation can be used to direct cost-effective quality improvement efforts to the high-risk modules. Tree-based software quality classification models based on software metrics are used to predict whether a software module is fault-prone or not fault-prone. They are white box quality estimation models with good accuracy, and are simple and easy to interpret. An in-depth study of calibrating classification trees for software quality estimation u
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
Abstract:
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, th
APA, Harvard, Vancouver, ISO, and other styles
6

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 (2021): 929–48. http://dx.doi.org/10.3233/ida-205361.

Full text
Abstract:
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 th
APA, Harvard, Vancouver, ISO, and other styles
7

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 (2013): 659–84. http://dx.doi.org/10.1162/evco_a_00101.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
8

Jun, Sungbum. "Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing." Sensors 21, no. 8 (2021): 2849. http://dx.doi.org/10.3390/s21082849.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
Abstract:
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 g
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
Abstract:
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 regressio
APA, Harvard, Vancouver, ISO, and other styles

Dissertations / Theses on the topic "Decision Tree with CART algorithm"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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

Full text
Abstract:
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 wit
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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 científ
APA, Harvard, Vancouver, ISO, and other styles
4

Kassim, M. E. "Elliptical cost-sensitive decision tree algorithm (ECSDT)." Thesis, University of Salford, 2018. http://usir.salford.ac.uk/47191/.

Full text
Abstract:
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 d
APA, Harvard, Vancouver, ISO, and other styles
5

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

Full text
Abstract:
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 perfo
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
<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 classification
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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 assoc
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Decision Tree with CART algorithm"

1

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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 Pola
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Decision Tree with CART algorithm"

1

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. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5640-8_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0479-2_243.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27172-4_64.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Decision Tree with CART algorithm"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
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 dat
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Decision Tree with CART algorithm"

1

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

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
2

Enhancing quality for clients: The balanced counseling strategy. Population Council, 2003. http://dx.doi.org/10.31899/rh2003.1014.

Full text
Abstract:
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 combinatio
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!