Relevant bibliographies by topics / ID3 Decision tree

Contents

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

Academic literature on the topic 'ID3 Decision tree'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "ID3 Decision tree"

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Chen, Xiao Juan, Zhi Gang Zhang, and Yue Tong. "An Improved ID3 Decision Tree Algorithm." Advanced Materials Research 962-965 (June 2014): 2842–47. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2842.

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As the classical algorithm of the decision tree classification algorithm, ID3 algorithm is famous for the merits of high classifying speed, strong learning ability and easy construction. But when used to make classification, the problem of inclining to choose attributions which have many values affect its practicality. This paper presents an improved algorithm based on the expectation information entropy and Association Function instead of the traditional information gain. In the improved algorithm, it modified the expectation information entropy with the improved Association Function and the
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M.Tamboli, N., A. M. Kamble, and P. S. Metkewar. "LCC Decision tree analysis using ID3." International Journal of Computer Applications 41, no. 19 (2012): 19–22. http://dx.doi.org/10.5120/5649-8007.

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Kirandeep and Neena Madan Prof. "Deployment of ID3 decision tree algorithm for placement prediction." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 740–44. https://doi.org/10.31142/ijtsrd11073.

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This paper details the ID3 classification algorithm. Very simply, ID3 builds a decision tree from a fixed set of examples. The resulting tree is used to classify future samples. The decision node is an attribute test with each branch to another decision tree being a possible value of the attribute. ID3 uses information gain to help it decide which attribute goes into a decision node. The main aim of this paper is to identify relevant attributes based on quantitative and qualitative aspects of a students profile such as CGPA, academic performance, technical and communication skills and design a
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Avcı, Keziban. "Evaluation of Public Hospitals' Performance with Decision Tree Algorithms." Verimlilik Dergisi 59, no. 1 (2025): 133–42. https://doi.org/10.51551/verimlilik.1494277.

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Purpose: The study aims to evaluate a range of financial performance indicators calculated through structural, operational, and HVI measures for public hospitals in the Turkish healthcare sector using various decision tree algorithms. Methodology: The study comprises threa phases. In the first phase, financial ratios were calculated from the hospitals' financial statements using the ratio analysis method. In the second phase, these ratios were used to calculate the HVI. In the third phase, the selected operational and financial indicators were analyzed with decision tree algorithms. The ID3, C
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Fakir, Y., M. Azalmad, and R. Elaychi. "Study of The ID3 and C4.5 Learning Algorithms." Journal of Medical Informatics and Decision Making 1, no. 2 (2020): 29–43. http://dx.doi.org/10.14302/issn.2641-5526.jmid-20-3302.

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Data Mining is a process of exploring against large data to find patterns in decision-making. One of the techniques in decision-making is classification. Data classification is a form of data analysis used to extract models describing important data classes. There are many classification algorithms. Each classifier encompasses some algorithms in order to classify object into predefined classes. Decision Tree is one such important technique, which builds a tree structure by incrementally breaking down the datasets in smaller subsets. Decision Trees can be implemented by using popular algorithms
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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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Li, Rong Xiang, Zeng Lei Zhang, Yun Liu, and Shan Chao Tu. "Applications of Data Mining Algorithm in Equipment Fault Diagnosis." Applied Mechanics and Materials 644-650 (September 2014): 2551–55. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2551.

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The Basic Principles of Data mining Decision-tree ID3 is opened out. The main deficiencies are analysed. An improved algorithm based on the ID3 is calculated. For fault diagnosis of engine exemple, traditional ID3 algorithm and the improved algorithm are applied to estimate the fault diagnosis of engine separately. Decision Trees of traditional ID3 algorithm and the improved algorithm are construct. Experiment result display the accuracy of improved algorithm is better than traditional ID3. The improved algorithm is more fit to applied to the equipment fault diagnosis.
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Ariestya, Winda Widya, Yulia Eka Praptiningsih, and Wahyu Supriatin. "DECISION TREE LEARNING UNTUK PENENTUAN JALUR KELULUSAN MAHASISWA." Jurnal Ilmiah FIFO 8, no. 1 (2016): 97. http://dx.doi.org/10.22441/fifo.v8i1.1304.

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Jalur kelulusan merupakan hal penentu seorang mahasiswa untuk memperoleh gelar jenjang pendidikan strata satu pada sebuah Perguruan Tinggi. Penelitian ini bertujuan untuk mengetahui pemanfaatan algoritma ID3 dalam penentu jalur kelulusan serta menghasilkan rule atau aturan pada penentuan jalur kelulusan mahasiswa tingkat akhir. Metode yang digunakan adalah Iterative Dichotomiser 3 (ID3) dengan menggunakan alat bantu perangkat lunak Rapidminer. Dari hasil penelitian diperoleh 7 rule atau aturan dalam penentuan jalur kelulusan yaitu 3 rule untuk jalur skipsi dan 4 rule untuk jalur non skripsi da
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Ilayani, Nangi Jumadil, and Purnamasari Pasrun Yuwanda. "APLIKASI DATA MINING UNTUK PENILAIAN KREDIT MENGGUNAKAN DECISION TREE ALGORITMA ID3 STUDI KASUS PT. MANDALA MULTI FINANCE CABANG KENDARI." semanTIK 4, no. 1 (2018): 65–76. https://doi.org/10.5281/zenodo.1402830.

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Credit is one of the most common payment mechanisms in the community. Data mining can search the pattern and new knowledge of these data so that it can facilitate in determination of debtor candidate. For that required a data mining applications that can process and provide the decision of the prospective debtor's eligibility status automatically. One method of data mining used for the classification of prospective borrowers is the decision tree using Iterative Algorithm Dichotomiser 3 (ID3). ID3 is one of the most popular types of Decision trees that attempt to build a decision tree top-d
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Nurtiani, Devy Natalia, and Rizal Adi Saputra. "KLASIFIKASI TINGKAT KEPUASAN MAHASISWA TERHADAP FASILITAS KAMPUS MENGGUNAKAN METODE ID3." METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi 8, no. 1 (2024): 123–28. http://dx.doi.org/10.46880/jmika.vol8no1.pp123-128.

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This research explores the level of student satisfaction with campus facilities using the ID3 (Iterative Dichotomiser 3) method in forming decision trees. The main focus of this research is to identify factors that influence student satisfaction with various facilities on campus such as libraries, computer laboratories, classrooms, sports facilities, and campus facilities in general. Data from 500 students were obtained from questionnaires and used as a basis for forming a decision tree model. The model visualization provides a clear picture of the satisfaction level classification pattern. Th
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Dissertations / Theses on the topic "ID3 Decision tree"

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Abu-halaweh, Nael Mohammed. "Integrating Information Theory Measures and a Novel Rule-Set-Reduction Tech-nique to Improve Fuzzy Decision Tree Induction Algorithms." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/cs_diss/48.

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Machine learning approaches have been successfully applied to many classification and prediction problems. One of the most popular machine learning approaches is decision trees. A main advantage of decision trees is the clarity of the decision model they produce. The ID3 algorithm proposed by Quinlan forms the basis for many of the decision trees’ application. Trees produced by ID3 are sensitive to small perturbations in training data. To overcome this problem and to handle data uncertainties and spurious precision in data, fuzzy ID3 integrated fuzzy set theory and ideas from fuzzy logic with
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Chida, Anjum A. "Protein Tertiary Model Assessment Using Granular Machine Learning Techniques." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/cs_diss/65.

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The automatic prediction of protein three dimensional structures from its amino acid sequence has become one of the most important and researched fields in bioinformatics. As models are not experimental structures determined with known accuracy but rather with prediction it’s vital to determine estimates of models quality. We attempt to solve this problem using machine learning techniques and information from both the sequence and structure of the protein. The goal is to generate a machine that understands structures from PDB and when given a new model, predicts whether it belongs to the same
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Svoboda, Pavel. "Metody klasifikace www stránek." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236643.

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The main goal of this master's thesis was to study the main principles of classification methods. Basic principles of knowledge discovery process, data mining and using an external class CSSBox are described. Special attantion was paid to implementation of a ,,k-nearest neighbors`` classification method. The first objective of this work was to create training and testing data described by 'n' attributes. The second objective was to perform experimental analysis to determine a good value for 'k', the number of neighbors.
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Rasmussen, Erin M., and Erin M. Rasmussen. "Improving Patient Safety and Incident Reporting Through Use of the Incident Decision Tree." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/626648.

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Background: Preventable medical error accounts for approximately 98,000 deaths in the hospital setting each year. A proposed solution to decreasing medical error encompasses the development of a culture of safety. Safety culture has been defined as a common set of values and beliefs that are shared by individuals within an organization that influence their actions and behaviors. In 2015, the safety culture of Registered Nurses (RN) and Patient Care Technicians (PCT) who regularly worked in the Intensive Care Unit (ICU) and Cardiovascular Intensive Care Unit (CVICU) at Flagstaff Medical Center
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Patera, Jan. "Rozhodovací stromy." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217653.

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This diploma thesis presents description on several algorithms for decision trees induction and software RapidMiner. The first part of the thesis deals with partition and terminology of decision trees. There’re described all algorithms for decision tree construction in RapidMiner. The second part deals with implementation and comparison of chosen algorithms. The application was developed in C++. Based on the real datesets the comparisson of different algorithms was realized using Rapid Miner 4.0.
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Flöter, André. "Analyzing biological expression data based on decision tree induction." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=978444728.

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Ferreira, Pedro Henrique Matheus da Costa. "Análise de dados de bases de honeypots: estatística descritiva e regras de IDS." Universidade Presbiteriana Mackenzie, 2015. http://tede.mackenzie.br/jspui/handle/tede/1460.

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Made available in DSpace on 2016-03-15T19:37:56Z (GMT). No. of bitstreams: 1 PEDRO HENRIQUE MATHEUS DA COSTA FERREIRA.pdf: 2465586 bytes, checksum: c81a1527d816aeb0b216330fd4267b93 (MD5) Previous issue date: 2015-03-04<br>Fundação de Amparo a Pesquisa do Estado de São Paulo<br>A honeypot is a computer security system dedicated to being probed, attacked or compromised. The information collected help in the identification of threats to computer network assets. When probed, attacked and compromised the honeypot receives a sequence of commands that are mainly intended to exploit a vulnerability
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Beck, Jason. "Implementation and Experimentation with C4.5 Decision Trees." Honors in the Major Thesis, University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/1157.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.<br>Bachelors<br>Engineering and Computer Science<br>Computer Engineering
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Zhong, Mingyu. "AN ANALYSIS OF MISCLASSIFICATION RATES FOR DECISION TREES." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2496.

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The decision tree is a well-known methodology for classification and regression. In this dissertation, we focus on the minimization of the misclassification rate for decision tree classifiers. We derive the necessary equations that provide the optimal tree prediction, the estimated risk of the tree's prediction, and the reliability of the tree's risk estimation. We carry out an extensive analysis of the application of Lidstone's law of succession for the estimation of the class probabilities. In contrast to existing research, we not only compute the expected values of the risks but also calcul
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Mann, Anna, and Olivia Höft. "Categorization of Swedish e-mails using Supervised Machine Learning." Thesis, KTH, Hälsoinformatik och logistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296558.

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Society today is becoming more digitalized, and a common way of communication is to send e-mails. Currently, the company Auranest has a filtering method for categorizing e-mails, but the method is a few years old. The filter provides a classification of valuable e-mails for jobseekers, where employers can make contact. The company wants to know if the categorization can be performed with a different method and improved. The degree project aims to investigate whether the categorization can be proceeded with higher accuracy using machine learning. Three supervised machine learning algorithms, Na
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Books on the topic "ID3 Decision tree"

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Informe regional de SIREVA II, 2017. Organización Panamericana de la Salud, 2020. http://dx.doi.org/10.37774/9789275323076.

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La vigilancia pasiva por laboratorio de Streptococcus pneumoniae se realiza en los países de la Región de las Américas desde 1993 bajo la denominación de Sistema Regional de Vacunas (SIREVA), con el apoyo de la Organización Panamericana de la Salud. En 1997, los países de la Región propusieron introducir las pruebas de laboratorio para Haemophilus influenzae, y en el 2000, para Neisseria meningitidis. Así se amplió la vigilancia por laboratorio de las enfermedades bacterianas invasivas con los tres patógenos anteriores, esta vez bajo la denominación de SIREVA II. La red está compuesta por 19 l
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Book chapters on the topic "ID3 Decision tree"

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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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Li, Ren-dong. "Stock Analysis Based on Decision Tree-Based ID3 Algorithm." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40618-8_29.

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

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Es-sabery, Fatima, Khadija Es-sabery, and Abdellatif Hair. "A MapReduce Improved ID3 Decision Tree for Classifying Twitter Data." In Business Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76508-8_13.

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Sathyadevan, Shiju, and Remya R. Nair. "Comparative Analysis of Decision Tree Algorithms: ID3, C4.5 and Random Forest." In Computational Intelligence in Data Mining - Volume 1. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2205-7_51.

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Pang, Chao. "Application of Decision Tree ID3 Algorithm in Tax Policy Document Recognition." In The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89508-2_23.

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Putri, Nastiti Susetyo Fanany, Aji Prasetya Wibawa, Harits Ar Rosyid, Andrew Nafalski, and Eisuke Hanada. "Immunology and Microbiology Journals Quartile Classification Using Decision Tree (ID3) Ensemble Models." In The Spirit of Recovery. CRC Press, 2023. http://dx.doi.org/10.1201/9781003331674-20.

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Xu, Jiaqi. "Design of Public Health Incident Warning System Based on Decision Tree Model ID3 Algorithm." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1157-8_13.

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Zhong, Xueli. "The Application of Decision Tree ID3 Algorithm in the Analysis of Enterprise Marketing Strategy." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9376-3_47.

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Shivakumar, B. R., B. G. Nagaraja, and G. Thimmaraja Yadava. "Classification Performance Analysis of CART and ID3 Decision Tree Classifiers on Remotely Sensed Data." In Advances in VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4444-6_8.

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Conference papers on the topic "ID3 Decision tree"

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Chi, Dianwei, Zehao Jia, Tiantian Huang, et al. "An improvement of a decision tree ID3 algorithm." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051346.

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Lu, Jiaxin. "Enhancing Chatbot User Satisfaction: A Machine Learning Approach Integrating Decision Tree, TF-IDF, and BERTopic." In 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2024. https://doi.org/10.1109/icpics62053.2024.10796445.

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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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Zou, Kaiqi, Wenming Sun, Hongzhi Yu, and Fengxin Liu. "ID3 Decision Tree in Fraud Detection Application." In 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE). IEEE, 2012. http://dx.doi.org/10.1109/iccsee.2012.241.

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Thakur, Devashsish, Nisarga Markandaiah, and D. Sharan Raj. "Re optimization of ID3 and C4.5 decision tree." In 2010 International Conference on Computer and Communication Technology (ICCCT). IEEE, 2010. http://dx.doi.org/10.1109/iccct.2010.5640492.

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Sitanggang, Imas Sukaesih, Razali Yaakob, Norwati Mustapha, and Ahmad Ainuddin B. Nuruddin. "An extended ID3 decision tree algorithm for spatial data." In 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM). IEEE, 2011. http://dx.doi.org/10.1109/icsdm.2011.5969003.

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Xie, Xiao-Lan, Zhen Long, and Wen-Qi Liao. "Improvement and analysis of ID3 algorithm in decision-making tree." In International Conference on Intelligent Earth Observing and Applications, edited by Guoqing Zhou and Chuanli Kang. SPIE, 2015. http://dx.doi.org/10.1117/12.2222194.

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Yang, Na, and Fei Li. "Decision Tree Model ID3 Algorithm in Enterprise Project Management System." In 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS). IEEE, 2023. http://dx.doi.org/10.1109/icicacs57338.2023.10099651.

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Charoenporn, Pattama. "Reservoir inflow forecasting using ID3 and C4.5 decision tree model." In 2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE). IEEE, 2017. http://dx.doi.org/10.1109/ccsse.2017.8088023.

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Liang, Xian, Fuheng Qu, Yong Yang, and Hua Cai. "An Improved ID3 Decision Tree Algorithm Based on Attribute Weighted." In 2nd International Conference on Civil, Materials and Environmental Sciences. Atlantis Press, 2015. http://dx.doi.org/10.2991/cmes-15.2015.167.

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Reports on the topic "ID3 Decision tree"

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Minatta, Alejandro, and Marcello Basani. Toda gestión requiere indicadores: ¡elabora los de tu innovación! Guía para la puesta en práctica en prestadores de servicios de agua, saneamiento y residuos sólidos en América Latina y el Caribe. Inter-American Development Bank, 2025. https://doi.org/10.18235/0013509.

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El fomento de la innovación en el de sector agua, saneamiento y residuos sólidos (AyS) resulta esencial para, entre otros propósitos, contribuir a la consecución, efectiva, rápida y eficiente de los Objetivos de Desarrollo Sostenible relacionados con agua y saneamiento (ODS 6) y con residuos sólidos (ODS 12). Sin embargo, el sector en América Latina y el Caribe enfrenta una serie de desafíos para innovar que se concentran en los ámbitos de gobernanza sectorial; investigación, desarrollo e innovación (IDi), o ecosistema de innovación sectorial; y prestación del servicio. El presente documento a
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99mTc SPECT-CT, Consensus QIBA Profile. Chair Yuni Dewaraja and Robert Miyaoka. Radiological Society of North America (RSNA)/Quantitative Imaging Biomarkers Alliance (QIBA), 2019. https://doi.org/10.1148/qiba/20191021.

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The quantification of 99mTc labeled biomarkers can add unique value in many different settings, ranging from clinical trials of investigation new drugs to the treatment of individual patients with marketed therapeutics. For example, goals of precision medicine include using companion radiopharmaceutical diagnostics as just-in-time, predictive biomarkers for selecting patients to receive targeted treatments, customizing doses of internally administered radiotherapeutics, and assessing responses to treatment. This Profile describes quantitative outcome measures that represent proxies of target c
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