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Keyword

C5.0 algorithm

Description: The C5.0 algorithm is a decision-tree machine-learning method used to classify data and identify patterns from predictor variables.
OpenAlex ID: keywords/c5-0-algorithm
Development of a preoperative predictive model for major complications following adult spinal deformity surgery
2017 · Justin K. Scheer, Justin S. Smith, et al. · Journal of Neurosurgery Spine · 162
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Decision tree analysis of construction fall accidents involving roofers
2014 · Gulgun Mistikoglu, Ibrahim Halil Gerek, et al. · Expert Systems with Applications · 162
Analisis Komparasi Metode Klasifikasi Data Mining dan Reduksi Atribut Pada Data Set Penyakit Jantung
2020 · Dito Putro Utomo, Mesran Mesran · JURNAL MEDIA INFORMATIKA BUDIDARMA · 134
A method for classification of network traffic based on C5.0 Machine Learning Algorithm
2012 · Tomasz Bujlow, Tahir Riaz, et al. · 2012 International Conference on Computing, Networking and Communications (ICNC) · 132
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Development of Validated Computer-based Preoperative Predictive Model for Proximal Junction Failure (PJF) or Clinically Significant PJK With 86% Accuracy Based on 510 ASD Patients With 2-year Follow-up
2016 · Justin K. Scheer, Joseph A. Osorio, et al. · Spine · 130
C5.0 Classification Algorithm and Application on Individual Credit Evaluation of Banks
2009 · Su-lin PANG, Ji-zhang GONG · Systems Engineering - Theory & Practice · 115
Assessment of Groundwater Potential Based on Multicriteria Decision Making Model and Decision Tree Algorithms
2016 · Huajie Duan, Zhengdong Deng, et al. · Mathematical Problems in Engineering · 112
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Optimal tree-based release rules for real-time flood control operations on a multipurpose multireservoir system
2008 · Chih-Chiang Wei, Nien-Sheng Hsu · Journal of Hydrology · 94
Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques
2011 · Shu-Guang He, Zhen He, et al. · Journal of Intelligent Manufacturing · 59
Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks
2020 · Peter Appiahene, Yaw Marfo Missah, et al. · Advances in Fuzzy Systems · 51
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CypRules: a rule-based P450 inhibition prediction server
2015 · Chi-Yu Shao, Bo-Han Su, et al. · Bioinformatics · 43
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Applying non-parametric models to explore urban life satisfaction in European cities
2020 · Mehdi Moeinaddini, Zohreh Asadi-Shekari, et al. · Cities · 38
Potential of predictive computer models for preoperative patient selection to enhance overall quality-adjusted life years gained at 2-year follow-up: a simulation in 234 patients with adult spinal deformity
2017 · Taemin Oh, Justin K. Scheer, et al. · Neurosurgical FOCUS · 38
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Rule-Based Prediction Models of Cytochrome P450 Inhibition
2015 · Bo-Han Su, Yi-shu Tu, et al. · Journal of Chemical Information and Modeling · 38
Web-based Classification Application for Forest Fire Data Using the Shiny Framework and the C5.0 Algorithm
2016 · Gita Puspita Siknun, Imas Sukaesih Sitanggang · Procedia Environmental Sciences · 32
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Improved random forest classification model combined with C5.0 algorithm for vegetation feature analysis in non-agricultural environments
2024 · Tianyu Wang · Scientific Reports · 30
Using Decision Tree to Predict Response Rates of Consumer Satisfaction, Attitude, and Loyalty Surveys
2019 · Jian Han, Miaodan Fang, et al. · Sustainability · 30
Analysis of students' performance evaluation using classification techniques
2016 · V. Shanmugarajeshwari, R. Lawrance · 28
Diagnostic performance of classification trees and hematological functions in hematologic disorders: an application of multidimensional scaling and cluster analysis
2021 · Fakher Rahim, Anoshirvan Kazemnejad, et al. · BMC Medical Informatics and Decision Making · 26
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Comparing Three Data Mining Methods to Predict Kidney Transplant Survival
2016 · Leila Shahmoradi, Mostafa Langarizadeh, et al. · Acta Informatica Medica · 26
Combining random forest and support vector machines for object-based rural-land-cover classification using high spatial resolution imagery
2019 · Saiping Xu, Qianjun Zhao, et al. · Journal of Applied Remote Sensing · 25
Reduksi Atribut Pada Dataset Penyakit Jantung dan Klasifikasi Menggunakan Algoritma C5.0
2020 · Dito Putro Utomo, Pahala Sirait, et al. · JURNAL MEDIA INFORMATIKA BUDIDARMA · 24
Defect Cause Modeling with Decision Tree and Regression Analysis
2008 · Berna Bakir, İnci Batmaz, et al. · OpenMETU (Middle East Technical University) · 24
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Survival Prediction of Patients with Breast Cancer: Comparisons of Decision Tree and Logistic Regression Analysis
2018 · Somayeh Momenyan, Ahmad Reza Baghestani, et al. · International Journal of Cancer Management · 22
Application of Data Mining Technique for Diagnosis of Posterior Uveal Melanoma
2002 · Darius Jegelevičius, Arūnas Lukoševičius, et al. · Lithuanian University of Health Sciences · 22
Works count: 411
Citation count: 2,451
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