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Статті в журналах з теми "ID3 Algorithm"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Li, Ju Fang, Jin Hui Lei, Xiao Xia Zhao, Chang Chang Zhang, and Xue Xue Han. "An Improved ID3 Algorithm." Applied Mechanics and Materials 444-445 (October 2013): 723–27. http://dx.doi.org/10.4028/www.scientific.net/amm.444-445.723.

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Анотація:

ID3 algorithm is the earliest and most influential of decision tree algorithm. This paper discusses the basic idea and implementation methods of the ID3 algorithm. A new algorithm based on attribute similarity for multivalued bias of ID3 algorithm was proposed, and the experimental results proved that the improved algorithm has strong predictive accuracy and better understandability.

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2

Henisaniyya, Nabila, Citra Pertiwi, Anita Desiani, Ali Amran, and Muhammad Arhami. "Classification of Thyroid Class using ID3 Algorithm and Artificial Neural Network (ANN)." SISTEMASI 14, no. 1 (2025): 1. https://doi.org/10.32520/stmsi.v14i1.3440.

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Thyroid disease refers to a range of conditions or issues affecting the thyroid gland. This gland, located below the Adam’s apple, is responsible for coordinating various metabolic processes in the body, making its function essential. Early detection of thyroid symptoms is crucial as an initial step in planning the necessary treatments to prevent more severe thyroid-related health risks. One commonly applied method for early detection involves classification using a data mining approach. Among the algorithms frequently used for classification are the ID3 algorithm and Artificial Neural Network

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3

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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Анотація:

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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4

Singh, Suman. "IDS with Hybrid ID3 Algorithm." International Journal of Computer Applications 64, no. 17 (2013): 12–15. http://dx.doi.org/10.5120/10725-5656.

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5

Huang, Ke, and Tingxuan Wang. "Optimized Application of the Decision Tree ID3 Algorithm Based on Big Data in Sports Performance Management." International Journal of e-Collaboration 20, no. 1 (2024): 1–20. http://dx.doi.org/10.4018/ijec.350022.

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In the rapidly evolving landscape of sports performance management, the integration of Big Data analytics has become a game-changer. This paper established a sports performance management system on big data decision tree ID3 algorithm and analyzed the accuracy and time of the improved decision tree ID3 algorithm in sports performance management. The research results show that the data query and data display functions are widely used in the system, and the utilization rate of each function of big data in the system can be clearly seen. In the accuracy of sports performance management, the impro

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6

Ju, Li, Lei Huang, and Sang-Bing Tsai. "Online Data Migration Model and ID3 Algorithm in Sports Competition Action Data Mining Application." Wireless Communications and Mobile Computing 2021 (July 10, 2021): 1–11. http://dx.doi.org/10.1155/2021/7443676.

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Анотація:

The ID3 algorithm is a key and important method in existing data mining, and its rules are simple and easy to understand and have high application value. If the decision tree algorithm is applied to the online data migration of sports competition actions, it can grasp the sports competition rules in the relationship between massive data to guide sports competition. This paper analyzes the application performance of the traditional ID3 algorithm in online data migration of sports competition actions; realizes the application steps and data processing process of the traditional ID3 algorithm, in

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7

Arao, Masaki, Masahito Tanaka, and Shigeyasu Kawaji. "Knowledge Acquisition by Improved Fuzzy ID3 Algorithm and Stability Analysis for Jacket Tank Temperature Control." Journal of Robotics and Mechatronics 12, no. 6 (2000): 682–88. http://dx.doi.org/10.20965/jrm.2000.p0682.

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The extraction of knowledge from operation data is an important theme in an autonomous control system. An efficient method for making a decision tree for classification from data is the fuzzy ID3 algorithm using fuzzy sets. However, the definition of fuzzy sets greatly affects the generation of fuzzy trees. In this paper, we propose a new version of fuzzy ID3 algorithms to generate a fuzzy decision maximizing the expected value of transferred information by applying a random search method for determining the fuzzy set, and by using the improved fuzzy ID3 algorithm an automatic extraction of co

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8

Rohman, Ali, Isturom Arif, Isnin Harianti, et al. "ID3 algorithm approach for giving scholarships." Journal of Physics: Conference Series 1175 (March 2019): 012116. http://dx.doi.org/10.1088/1742-6596/1175/1/012116.

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9

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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Анотація:

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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10

Huang, Pingge. "Construction of College Chinese Blended Teaching Mode Based on Decision Tree Classification Model in New Media Context." Computational Intelligence and Neuroscience 2022 (September 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/4608631.

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Анотація:

Based on the context of new media and big data, this article uses the decision tree classification model to construct the college Chinese hybrid teaching mode. In order to verify the accuracy of ID3 algorithm prediction, the comparison of the ID3 algorithm, K-means algorithm, and support vector machine classification algorithm was made, and the experimental results show that the ID3 decision tree classification algorithm has better prediction and classification ability, for the construction of college Chinese hybrid teaching mode provides certain practical value and reference basis.

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11

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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Анотація:

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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12

Karo, I. M. K., M. Y. Fajari, N. U. Fadhilah, and W. Y. Wardani. "Benchmarking Naïve Bayes and ID3 Algorithm for Prediction Student Scholarship." IOP Conference Series: Materials Science and Engineering 1232, no. 1 (2022): 012002. http://dx.doi.org/10.1088/1757-899x/1232/1/012002.

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Abstract Student scholarship through Indonesian smart cards (ISC) is a cash donation scholarship for all student in range 6-21 years old as a solution of destitute child or potential dropouts. Currently the large student data stored in educational database. The data was used to determine feasible receiver ISC. Manual prediction by human take long time and potentially human error. This paper aim to predict feasible receiver ISC by using educational data mining. Source of data come from a senior high school database in Riau Province, Indonesia. In this paper we compared two algorithms (Naïve Bay

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13

Sheng, Yu Kui, and Wan Lian Lan. "Application of ID3 Algorithm in Logistics Performance Evaluation." Key Engineering Materials 480-481 (June 2011): 723–26. http://dx.doi.org/10.4028/www.scientific.net/kem.480-481.723.

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Анотація:

It is of vital importance to evaluate logistics performance of logistics company along with fiercer competition. ID3 algorithm is an effective method. In this paper, fisrtly we introduce ID3 algorithm. Then we apply it to evaluate logistics performance of a logistics company.

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14

Pratama, Handika Syayuti. "IMPLEMENTASI ALGORITMA C 4.5 DAN ALGORITMA ID3 UNTUK MENGANALISIS KEPUASAN PELANGGAN TERHADAP PELAYANAN BENGKEL KENDARAAN BERMOTOR." Jurnal Informatika: Jurnal Pengembangan IT 4, no. 3 (2019): 212–18. http://dx.doi.org/10.30591/jpit.v4i3.1537.

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AbstractThe growth of motor vehicle repair services business is a business that can be said to have a very high growth in Indonesia. This is due to the large number of motor vehicles in the country. Customer satisfaction in a business is important because of the satisfaction of the customer who will move the business forward. In this study results of consumer satisfaction survey in the workshop was analyzed using classification method with C 4.5 algorithm and Iterative Dichotomiser algorithm 3 (ID3). The results of the C 4.5 algorithm and the Iterative Dichotomiser algorithm 3 (ID3) Customers

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15

Che, Yue. "Diagnosis of hepatitis C virus based on ID3 algorithm." Applied and Computational Engineering 6, no. 1 (2023): 1503–9. http://dx.doi.org/10.54254/2755-2721/6/20230736.

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Infection with the hepatitis C virus (HCV) is what causes hepatitis C. China has one of the highest hepatitis C infection rates in the world (13%-15%), and this illness affects more than 170 million people worldwide. As a result, there is an enormous need for the diagnosis of this disease. In recent years, researchers have made significant progress in this field with different machine learning algorithms and have had the ability to make a relatively precise diagnosis. However, within the machine learning algorithms that the researchers used, decision trees, particularly the ID3(Iterative Dicho

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16

Chen, Wenzheng, Guanglei Yang, and Dongkai Qi. "Comprehensive Evaluation of College Students’ Physical Health and Sports Mode Recommendation Model Based on Decision Tree Classification Model." Computational Intelligence and Neuroscience 2022 (July 22, 2022): 1–8. http://dx.doi.org/10.1155/2022/5504850.

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Nowadays, more and more college students’ physical health is getting worse because of their living habits and self-consciousness. In order to improve the physical quality of college students as much as possible, the experiment uses the improved iterative dichotomiser III (ID3) decision tree to make decisions on the physical condition of some college students and the corresponding sports mode recommendation, and compares the results with the traditional ID3 algorithm. In the experimental results, the information entropy ratio of the improved ID3 algorithm is 89.5%, the operation information los

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17

Priyanka, Saini. "Decision Tree Algorithm Implementation Using Educational Data." International Journal of Computer-Aided technologies (IJCAx) 1, April (2021): 31–41. https://doi.org/10.5281/zenodo.5105645.

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Анотація:

There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.

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18

Priyanka, Saini1 Sweta Rai2 and Ajit Kumar Jain3 1. 2. M.Tech Student Banasthali University Tonk Rajasthan. "Decision Tree Algorithm Implementation Using Educational Data." International Journal of Computer-Aided technologies (IJCAx) 01, dec (2014): 01–11. https://doi.org/10.5281/zenodo.1450276.

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Анотація:

There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.

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19

Jiang, Ming Hua, and Xiao Suo Luo. "Classification of Student Achievement Using ID3 Algorithm." Applied Mechanics and Materials 220-223 (November 2012): 2540–45. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2540.

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20

Manna, Subhankar, and Malathi G. "PERFORMANCE ANALYSIS OF CLASSIFICATION ALGORITHM ON DIABETES HEALTHCARE DATASET." International Journal of Research -GRANTHAALAYAH 5, no. 8 (2017): 260–66. http://dx.doi.org/10.29121/granthaalayah.v5.i8.2017.2229.

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Healthcare industry collects huge amount of unclassified data every day. For an effective diagnosis and decision making, we need to discover hidden data patterns. An instance of such dataset is associated with a group of metabolic diseases that vary greatly in their range of attributes. The objective of this paper is to classify the diabetic dataset using classification techniques like Naive Bayes, ID3 and k means classification. The secondary objective is to study the performance of various classification algorithms used in this work. We propose to implement the classification algorithm using

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21

Subhankar, Manna, and G. Malathi. "PERFORMANCE ANALYSIS OF CLASSIFICATION ALGORITHM ON DIABETES HEALTHCARE DATASET." International Journal of Research - Granthaalayah 5, no. 8 (2017): 260–66. https://doi.org/10.5281/zenodo.890581.

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Анотація:

Healthcare industry collects huge amount of unclassified data every day. For an effective diagnosis and decision making, we need to discover hidden data patterns. An instance of such dataset is associated with a group of metabolic diseases that vary greatly in their range of attributes. The objective of this paper is to classify the diabetic dataset using classification techniques like Naive Bayes, ID3 and k means classification. The secondary objective is to study the performance of various classification algorithms used in this work. We propose to implement the classification algorithm using

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22

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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23

Qiang, Guangping, Lin Sun, and Qiang Huang. "ID3 algorithm and its improved algorithm in agricultural planting decision." IOP Conference Series: Earth and Environmental Science 474 (May 15, 2020): 032025. http://dx.doi.org/10.1088/1755-1315/474/3/032025.

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24

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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25

Febriantono, M. Aldiki, Sholeh Hadi Pramono, Rahmadwati Rahmadwati, and Golshah Naghdy. "Classification of multiclass imbalanced data using cost-sensitive decision tree C5.0." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 1 (2020): 65. http://dx.doi.org/10.11591/ijai.v9.i1.pp65-72.

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Анотація:

The multiclass imbalanced data problems in data mining were an interesting to study currently. The problems had an influence on the classification process in machine learning processes. Some cases showed that minority class in the dataset had an important information value compared to the majority class. When minority class was misclassification, it would affect the accuracy value and classifier performance. In this research, cost sensitive decision tree C5.0 was used to solve multiclass imbalanced data problems. The first stage, making the decision tree model uses the C5.0 algorithm then the

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26

M., Aldiki Febriantono, Hadi Pramono Sholeh, Rahmadwati, and Naghdy Golshah. "Classification of multiclass imbalanced data using cost-sensitive decision tree C5.0." International Journal of Artificial Intelligence (IJ-AI) 9, no. 1 (2020): 65–72. https://doi.org/10.11591/ijai.v9.i1.pp65-72.

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Анотація:

The multiclass imbalanced data problems in data mining were interesting cases to study currently. The problems had an influence on the classification process in machine learning processes. Some cases showed that minority class in the dataset had an important information value compared to the majority class. When minority class was misclassification, it would affect the accuracy value and classifier performance. In this research, cost sensitive decision tree C5.0 was used to solve multiclass imbalanced data problems. The first stage, making the decision tree model uses the C5.0 algorithm then t

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27

Sholikhah, Minhatin Nisaatus, Dinita Rahmalia, and Mohammad Syaiful Pradana. "Penerapan Algoritma ID3 dan Algoritma C4.5 Untuk Klasifikasi Penerima BPNT." Unisda Journal of Mathematics and Computer Science (UJMC) 9, no. 2 (2023): 21–28. http://dx.doi.org/10.52166/ujmc.v9i2.6111.

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Non-Cash Food Assistance (BPNT) is social food assistance in the form of non-cash from the government which is given to Beneficiary Families (KPM) every month through an electronic account mechanism which is used only to buy food at traders or e-warongs. One of the difficulties that the government sometimes faces in distributing BPNT is that the distribution process is uneven and not on target. Therefore, it is necessary to carry out further analysis using a mathematical approach, so that we can determine the feasibility of a BPNT recipient prediction problem. Through the results of the data c

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28

Yang, Weiwei, and Jie Yang. "Construction of College Physical Education MOOCS Teaching Model Based on Fuzzy Decision Tree Algorithm." Mathematical Problems in Engineering 2022 (September 27, 2022): 1–11. http://dx.doi.org/10.1155/2022/3315872.

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With the continuous development of the MOOCS model in college physical education, the corresponding teaching evaluation has also been widely concerned by the community. The development of a traditional education mode in college physical education cannot meet the current teaching requirements. In order to solve the problem of narrow application and insufficient accuracy in traditional education, on the basis of the Kohonen fuzzy decision tree algorithm and the MOOCS fuzzy decision tree algorithm, a fuzzy evaluation model of P.E. teaching is proposed. The results show that the fuzzy ID3 algorith

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29

Khin, Khin Lay, and San Nwe San. "Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1392–95. https://doi.org/10.5281/zenodo.3590845.

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Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Data mining technology which can related to this student grade well and we also used classification algorithms prediction. In this paper, we used educational data mining to predict students final grade based on their performance. We proposed st

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30

Wajhillah, Rusda. "PENERAPAN METODE ALGORITMA ID3 UNTUK PREDIKSI DIAGNOSA GAGAL GINJAL KRONIS (STUDI KASUS: RSUD SEKARWANGI SUKABUMI)." KLIK - KUMPULAN JURNAL ILMU KOMPUTER 6, no. 1 (2019): 97. http://dx.doi.org/10.20527/klik.v6i1.211.

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<p><em>The Iterative Dichotomiser 3 (ID3) algorithm is part of the classification method and is a type of method that can map or separate two or more different classes. One of the problems that can be solved using algorithm ID3 is the prediction of the diagnosis of chronic kidney disease. Chronic kidney disease is a failure of kidney function to maintain metabolism and fluid and electrolyte balance. Based on the classification performance measurement from 230 data training shows that the accuracy value reaches 96,08%. It can be concluded that the method of ID3 Algorithm is feasible

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31

Chen, Lin, Xiaolong Chen, Hongxin Wang, Lin Zhu, and Lingyun Lang. "Research on Spatial and Dynamic Planning Methods for Settlement Buildings Based on Data Mining." Discrete Dynamics in Nature and Society 2022 (January 13, 2022): 1–11. http://dx.doi.org/10.1155/2022/3528605.

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Traditional settlements are widely concerned by academic circles for their unique settlement patterns, exquisite residential buildings, and rich historical and cultural connotations, and their protection and development is an important proposition for rural revitalization. Therefore, from the perspective of big data mining (BDM), this paper explores its application in architectural space and settlement protection of traditional settlements in Hainan and provides new ideas for the protection and renewal of traditional settlements in Hainan. The attribute elements of spatial data of settlement g

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32

Begenova, S. B., and T. V. Avdeenko. "Building of fuzzy decision trees using ID3 algorithm." Journal of Physics: Conference Series 1015 (May 2018): 022002. http://dx.doi.org/10.1088/1742-6596/1015/2/022002.

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33

Yang, Shuo, Jing-Zhi Guo, and Jun-Wei Jin. "An improved Id3 algorithm for medical data classification." Computers & Electrical Engineering 65 (January 2018): 474–87. http://dx.doi.org/10.1016/j.compeleceng.2017.08.005.

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34

Pal, N. R., Sukumar Chakraborty, and A. Bagchi. "RID3: An ID3-like algorithm for real data." Information Sciences 96, no. 3-4 (1997): 271–90. http://dx.doi.org/10.1016/s0020-0255(96)00162-4.

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35

Schrodt, Philip A. "Predicting Interstate Conflict Outcomes Using a Bootstrapped ID3 Algorithm." Political Analysis 2 (1990): 31–56. http://dx.doi.org/10.1093/pan/2.1.31.

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Анотація:

The ID3 algorithm is an inductive artificial intelligence technique that generates classification trees. These trees are similar to those used in simple expert systems; with ID3 they are generated by machine rather than using human experts. This article applies a bootstrapped ID3 to the Butterworth data set on interstate conflict management. By generating a number of classification trees from randomly selected subsets of the complete data set, the variables that most effectively predict the outcome of the conflict management effort are identified, and the degree of unpredictability in the data

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36

Atallah, Dalia, Ali Eldesoky, Amira H., and Mohamed Ghoneim. "One-year renal graft survival prediction using a weighted decision tree classifier." International Journal of Engineering & Technology 3, no. 3 (2014): 327. http://dx.doi.org/10.14419/ijet.v3i3.2334.

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This study introduces a weighted decision tree algorithm for prediction of graft survival in renal transplantation using preoperative patient's data. The objective was to identify the preoperative attributes that affect the graft survival. Between the years 2000-2009, renal allotransplantation was carried out for 889 patients at Urology and Nephrology Center which is the subject matter of this study. The ID3 algorithm was chosen to build up the decision tree using the weka machine learning software. A modification was made on ID3 to refine the results. A weighted vector was introduced. The ele

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37

Yudhanegara, Reza Ardiansyah, Nisrina Aliya Hana, Syahrizal Yonanda Mahfiridho, and Aqwam Rosadi Kardian. "Perbandingan Resident Set Size dan Virtual Memory Size Algoritma Machine Learning dalam Analisis Sentimen." JURNAL MEDIA INFORMATIKA BUDIDARMA 8, no. 1 (2024): 371. http://dx.doi.org/10.30865/mib.v8i1.7201.

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In the rapidly advancing era of digital transformation, where textual data abounds from various online sources such as social media, forums, and product reviews, sentiment analysis has become a critical component in understanding public opinions and consumer behavior. Sentiment analysis employs machine learning, natural language processing, and computational linguistics to comprehend the feelings and opinions of others. The machine learning algorithms investigated in this paper include K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Naive Bayes, ID3, and C4.5. The sentiment analysis p

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Li, Mengjie, and Chao Liu. "Design of Intelligent Fire Alarm System Based on Multisensor Data Fusion." Mobile Information Systems 2022 (July 7, 2022): 1–12. http://dx.doi.org/10.1155/2022/6491577.

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With the rapid development of today’s alarm system, the market demand for an intelligent alarm system is increasing. The traditional alarm system needs technological progress/advancement to meet the needs of the society, and the alarm system needs to develop in the direction of integration, both digitally and professionally. The intelligent fire warning systems using integrated multisensor digital data integration techniques can obtain the data information of the measured object more accurately and comprehensively from multiple dimensions, to improve the system alarm accuracy. This article aim

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Grzymala-Busse, Jerzy W. "Selected Algorithms of Machine Learning from Examples." Fundamenta Informaticae 18, no. 2-4 (1993): 193–207. http://dx.doi.org/10.3233/fi-1993-182-408.

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This paper presents and compares two algorithms of machine learning from examples, ID3 and AQ, and one recent algorithm from the same class, called LEM2. All three algorithms are illustrated using the same example. Production rules induced by these algorithms from the well-known Small Soybean Database are presented. Finally, some advantages and disadvantages of these algorithms are shown.

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Aswathi, R. Raja, K. Pazhani Kumar, and B. Ramakrishnan. "An Extended C4.5 Classification Algorithm using Mathematical Series." Science & Technology Journal 7, no. 2 (2019): 54–59. http://dx.doi.org/10.22232/stj.2019.07.02.06.

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The algorithm C4.5 is an efficient decision tree based classification, which is derived from the ID3 approach. C4.5 is also a rule based classification algorithm. The main importance of the C4.5 algorithm is that it can deal with categorical data, over fitting of data and handling of missing values. The performance of C4.5 is superior to ID3 even with equal number of attributes. The EC4.5 (Exponential C4.5) is an extension of C4.5 algorithm which uses exponential of split value to predict the gain of attributes and handled the set back reported in C4.5. However the EC4.5 has some misclassifica

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Nuraini. "Implementasi Algoritma ID3 Pada Analisa Data Pengolahan Karet." Bulletin of Computer Science Research 4, no. 3 (2024): 308–17. http://dx.doi.org/10.47065/bulletincsr.v4i3.340.

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PT. Socfindo, rubber cultivation and production is one of the main activities, where production risks play a significant role. Fluctuating rubber production and productivity are among the consequences of production risks. The production and productivity of natural rubber at Aek Pamienke PT Socfindo have experienced fluctuations from 2009 until now. There have even been instances of unmet production targets at the company. This is attributed to several production risks, such as technological usage, rainfall, pests, and diseases, resulting in a yearly decrease in total rubber processing despite

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BALDWIN, J. F., and J. LAWRY. "A NEW APPROACH TO LEARNING LINGUISTIC CONTROL RULES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, no. 01 (2000): 21–43. http://dx.doi.org/10.1142/s0218488500000046.

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The mass assignment ID3 (MA-ID3) algorithm for generating linguistic decision trees is introduced together with the mass assignment semantics for linguistic variables. The potential of this algorithm for learning control rules is illustrated by means of the Van de Pol system. A data set of control paths is generated using an existing on-line controller. This is then used to generate a set of quantified linguistic control rules. The effectiveness and robustness of this rule-base is then demonstrated.

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Nur, Farahaina Idris, Arfian Ismail Mohd, Saberi Mohamad Mohd, Kasim Shahreen, Zakaria Zalmiyah, and Sutikno Tole. "Breast cancer disease classification using fuzzy-ID3 algorithm based on association function." International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 448–61. https://doi.org/10.11591/ijai.v11.i2.pp448-461.

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Breast cancer is the second leading cause of mortality among female cancer patients worldwide. Early detection of breast cancer is considerd as one of the most effective ways to prevent the disease from spreading and enable human can make correct decision on the next process. Automatic diagnostic methods were frequently used to conduct breast cancer diagnoses in order to increase the accuracy and speed of detection. The fuzzy-ID3 algorithm with association function implementation (FID3-AF) is proposed as a classification technique for breast cancer detection. The FID3-AF algorithm is a hybridi

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Aritonang, Milyani. "Penerapan Algoritma ID3 dalam Prediksi Kebutuhan Pupuk." Journal of Information System Research (JOSH) 2, no. 4 (2021): 247–53. http://dx.doi.org/10.47065/josh.v2i4.565.

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The need for fertilizer at the Plant Protection Development Unit (UPPT) is uncertain depending on the demand of farmers, therefore it is necessary to predict fertilizer needs. There are five types of fertilizers predicted by the Plant Protection Development Unit (UPPT), including Urea fertilizer, ZA fertilizer, SP-36 fertilizer, NPK fertilizer, and Organic fertilizer, so fertilizer needs can be predicted. In predicting data mining on fertilizer needs using the ID3 algorithm. Where it works is calculating the value of entropy and gain to get the final result in the form of a tree to the decisio

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Fitrani, A. S., M. A. Rosid, Y. Findawati, Y. Rahmawati, and A. K. Anam. "Implementation of ID3 algorithm classification using web-based weka." Journal of Physics: Conference Series 1381 (November 2019): 012036. http://dx.doi.org/10.1088/1742-6596/1381/1/012036.

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Jin, Chenxia, Fachao Li, and Yan Li. "A generalized fuzzy ID3 algorithm using generalized information entropy." Knowledge-Based Systems 64 (July 2014): 13–21. http://dx.doi.org/10.1016/j.knosys.2014.03.014.

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Idris, Nur Farahaina, and Mohd Arfian Ismail. "Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition." PeerJ Computer Science 7 (May 4, 2021): e427. http://dx.doi.org/10.7717/peerj-cs.427.

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Breast cancer becomes the second major cause of death among women cancer patients worldwide. Based on research conducted in 2019, there are approximately 250,000 women across the United States diagnosed with invasive breast cancer each year. The prevention of breast cancer remains a challenge in the current world as the growth of breast cancer cells is a multistep process that involves multiple cell types. Early diagnosis and detection of breast cancer are among the greatest approaches to preventing cancer from spreading and increasing the survival rate. For more accurate and fast detection of

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Alkwai, Lulwah M. "Enhancing Diagnostic Accuracy of Co-occurring Diabetic and Thyroid Diseases using Machine Learning Techniques." Journal of Electrical Systems 20, no. 7s (2024): 495–505. http://dx.doi.org/10.52783/jes.3344.

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Efficient classification methods are crucial for accurately predicting co-occurring diabetic and thyroid diseases, addressing substantial global health challenges. These conditions affect individuals across diverse demographics, including males, females, infants, adolescents, and the elderly. This study employs ML algorithms to forecast co-occurring diabetic and thyroid diseases (DTD). Utilizing a dataset sourced from the UCI Machine Learning Repository, feature selection techniques were applied to identify relevant attributes and optimize predictive accuracy. Seven distinct machine learning a

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Yohanssen, Pratama, and Sutanto Saragi Hadi. "Cassava Quality Classification for Tapioca Flour Ingredients by Using ID3 Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (2018): 799–805. https://doi.org/10.11591/ijeecs.v9.i3.pp799-805.

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Cassava is one of the main foods consumed by Indonesian people and main ingredients to make tapioca flour. In North Sumatera there is factory that produced tapioca flour to fulfill consumer demand. To be able to meet the needs of consumers and seize market share, the product must have a good quality. Product specifications are a reference for product quality and measured with 7 parameters. The seven parameters include whiteness, moisture content, spotness, ash content, thinness, residual screen, pH flour, which meets the Indonesian National Standard. In this research we use two parameters (whi

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Tita Tosida, Eneng, Fajar Delli Wihartiko, and Indra Lumesa. "Learning Vector Quantization Implementation to Predict the Provision of Assistance for Indonesian Telematics Services SMES." International Journal of Engineering & Technology 7, no. 3.20 (2018): 381. http://dx.doi.org/10.14419/ijet.v7i3.20.20576.

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Implementation of Learning Vector Quantization (LVQ) Algorithm for classification of Indonesia telematics service is designed and created as a classification system to support the decision of grant aid for Small Medium Enterprises (SMEs). Based on the test results, the LVQ algorithm has the best accuracy (93.11%) when compared with ID3 algorithm (64%) and C45 (62%) for telematics data of National Census of Economic (Susenas 2006). The data is still valid and relevant for use in this research because in Indonesia census data is done every 10 years and there is no update of data until now. LVQ i

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