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Land Evaluation Method Based on Decision Tree Produced by C4.5 and Fuzzy Decision 认领 引用 被引量：6
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作者 杨敬锋 李亭 陈志民 《Agricultural Science & Technology》 CAS 2010年第3期1-3,27,共3页
[Objective]The aim was to overcome the shortage of being difficult to build land evaluation model when the impact factors had continuous value in the traditional land evaluation process,as well as to improve the intel... [Objective]The aim was to overcome the shortage of being difficult to build land evaluation model when the impact factors had continuous value in the traditional land evaluation process,as well as to improve the intelligibility of the land evaluation knowledge.[Method] The land evaluation method combining classification rule extracted by C4.5 algorithm with fuzzy decision was proposed in this study.[Result] The result of Second General Soil Survey of Guangdong Province had demonstrated that the method was convenient to extract classification rules,and by using only 100 rules,quantity correct rate 86.67% and area correct rate 84.80% of land evaluation could be obtained.[Conclusions] The use of C4.5 algorithm to obtain the rules,combined with fuzzy decision algorithm to build classifiers had got satisfactory results,which provided a practical algorithm for the land evaluation. 展开更多
关键词 Land Evaluation C4.5 Algorithm Fuzzy decision
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Application Comparison of Association Rules and C4.5 Rules in Land Evaluation 认领 引用 被引量：3
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作者 李亭 杨敬锋 陈志民 《Agricultural Science & Technology》 CAS 2010年第4期144-147,共4页
Association rules and C4.5 rules can overcome the shortage of the traditional land evaluation methods and improve the intelligibility and efficiency of the land evaluation knowledge.In order to compare these two kinds... Association rules and C4.5 rules can overcome the shortage of the traditional land evaluation methods and improve the intelligibility and efficiency of the land evaluation knowledge.In order to compare these two kinds of classification rules in the application,two fuzzy classifiers were established by combining with fuzzy decision algorithm especially based on Second General Soil Survey of Guangdong Province.The results of experiments demonstrated that the fuzzy classifier based on association rules obtain a higher accuracy rate,but with more complex calculation process and more computational overhead;the fuzzy classifier based on C4.5 rules obtain a slightly lower accuracy,but with fast computation and simpler calculation. 展开更多
关键词 Land evaluation Association rules C4.5 Algorithm Fuzzy decision
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基于Map-Reduce框架的C4.5分布式改进算法 认领 引用 被引量：2
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作者 廖绍雯 贾聪 《自动化与仪器仪表》 2020年第8期17-21,共5页
为了利用决策树分类算法解决大规模数据集上的分类问题,提出了一种基于经典C4.5决策树算法的分布式算法,在Map-Reduce分布式计算系统框架下,利用分布式思想构造决策树节点,解决了数据量大和数据复杂度高导致分类时运行速度慢的问题。仿... 为了利用决策树分类算法解决大规模数据集上的分类问题,提出了一种基于经典C4.5决策树算法的分布式算法,在Map-Reduce分布式计算系统框架下,利用分布式思想构造决策树节点,解决了数据量大和数据复杂度高导致分类时运行速度慢的问题。仿真结果表明,与经典C4.5算法相比,该分布式算法具有较高的分类准确率及较好的speedup、scaleup和sizeup性能。 展开更多
关键词 经典C4.5算法 大数据 分布式算法 Map-Reduce 准确率
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C4.5数据挖掘算法的改进及其应用 认领 引用 被引量：6
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作者 袁爱香 《山东农业大学学报（自然科学版）》 北大核心 2008年第3期461-464,共4页
简要介绍数据挖掘算法中的C4.5算法的基本思想,并在分析传统行业税负测算方法的基础上,结合税收行业领域的应用实际,对C4.5算法进行改进和应用。通过验证表明,改进后的算法运行结果可靠,运行效率提高。
关键词 C4.5算法 数据挖掘 改进应用
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A PREDICTION SCHEME FOR THE PRECIPITATION OF SPR BASED ON THE DATA MINING ALGORITHM AND CIRCULATION ANALYSIS 认领 引用 被引量：2
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作者 LI Chao SHI Da・wei +3 位作者 CHEN Yu-tian ZHANG Hong-hua GENG Huan-tong WANG Peng 《Journal of Tropical Meteorology》 SCIE 2019年第4期519-527,共9页
Based on the 74 circulation indexes provided by National Climate Center of China(NCC) and the 24 indexes compiled by National Oceanic and Atmospheric Administration(NOAA) of the US, the study used the C4.5 algorithm i... Based on the 74 circulation indexes provided by National Climate Center of China(NCC) and the 24 indexes compiled by National Oceanic and Atmospheric Administration(NOAA) of the US, the study used the C4.5 algorithm in data mining to establish a decision tree prediction model to predict whether the spring persistent rains(SPR) of 55 years(from 1961 to 2015) is more than the normal, and obtained 5 rules to determine whether the SPR is more than the normal. The accuracy rate of the test set, namely "whether the SPR is more than the normal", is 98.18%. After the evaluation of the model by conducting ten 10-fold cross validations to take the average value, the test accuracy rate gained is 84%. There are differences between the three types of years with a SPR more than the normal when it comes to intensity and distribution. In spring, they have respective anomalous 850 hPa monthly mean wind fields and water-vapor flux distribution, and 700 hPa forms the zone where the vertical speed is anomalously negative. As indicated by the results, the SPR prediction model based on the C4.5 algorithm has a high prediction accuracy rate, the model is reasonably and effectively constructed, and the decision rules take comprehensive factors into consideration. The anomalous rainfall and circulation distribution characteristics obtained based on the decision classification results provide new ideas and methods for the prediction of SPR. 展开更多
关键词 spring persistent rains data mining C4.5 algorithm prediction model model analysis
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Research on student performance prediction model based on decision tree algorithm 认领 引用
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作者 Yin Guan 《Advances in Engineering Innovation》 2026年第5期157-166,共10页
With the deepening of educational informatization,universities have accumulated massive amounts of student performance data.Extracting valuable information from these data to support instructional decisionmaking has b... With the deepening of educational informatization,universities have accumulated massive amounts of student performance data.Extracting valuable information from these data to support instructional decisionmaking has become an important research topic.This paper constructs a student performance prediction model based on the decision tree algorithm.First,the basic principles of decision tree classification techniques are systematically reviewed,with particular focus on the information gain ratio mechanism and post-pruning method of the C4.5 algorithm.Second,a complete prediction workflow is designed,encompassing data acquisition,preprocessing,feature selection,and model construction and evaluation.Third,an empirical analysis is conducted using student performance data from a university,resulting in a decision tree model with strong interpretability.Finally,the model's performance is evaluated using a confusion matrix.Experimental results indicate that the proposed decision tree model achieves a prediction accuracy exceeding 85%and can effectively identify key factors influencing student performance.The findings provide data support and decision-making references for teachers to improve instructional methods and for universities to optimize teaching management. 展开更多
关键词 data mining decision tree C4.5 algorithm student performance prediction classification rules
Research on Scholarship Evaluation System based on Decision Tree Algorithm 认领 引用 被引量：1
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作者 YIN Xiao WANG Ming-yu 《电脑知识与技术》 2015年第3X期11-13,共3页
Under the modern education system of China, the annual scholarship evaluation is a vital thing for many of the collegestudents. This paper adopts the classification algorithm of decision tree C4.5 based on the betteri... Under the modern education system of China, the annual scholarship evaluation is a vital thing for many of the collegestudents. This paper adopts the classification algorithm of decision tree C4.5 based on the bettering of ID3 algorithm and constructa data set of the scholarship evaluation system through the analysis of the related attributes in scholarship evaluation information.And also having found some factors that plays a significant role in the growing up of the college students through analysis and re-search of moral education, intellectural education and culture&PE. 展开更多
关键词 data mining scholarship evaluation system decision tree algorithm C4.5 algorithm
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面向乳腺肿瘤的诊前问答系统决策模型构建研究 认领 引用 被引量：2
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作者 王世文 李一凡 +1 位作者 郑群 曹旭晨 《医学信息学杂志》 CAS 2023年第8期54-59,65,共6页
目的/意义运用决策树分类模型模拟专家问诊思路,预测潜在或已有乳腺肿瘤患者的疾病风险。方法/过程采用C 4.5经典分类算法和悲观剪枝法,对调研收集的病例数据进行患者预问诊的结果预测。结果/结论生成一棵以“术后化疗or放疗在院是否结... 目的/意义运用决策树分类模型模拟专家问诊思路,预测潜在或已有乳腺肿瘤患者的疾病风险。方法/过程采用C 4.5经典分类算法和悲观剪枝法,对调研收集的病例数据进行患者预问诊的结果预测。结果/结论生成一棵以“术后化疗or放疗在院是否结束”为根节点、拥有76个叶子节点的C 4.5决策树,预测准确率达95%,并根据分类标签划分为3个风险等级。 展开更多
关键词 乳腺肿瘤 C 4.5算法 决策树 模型构建
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Some Pathological Knowledge Discovered in Large Database of Type 2 Diabetes 认领 引用
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作者 罗森林 高娟 +3 位作者 贾洪波 王恒 张铁梅 韩怡文 《Journal of Beijing Institute of Technology》 EI CAS 2007年第3期310-314,共5页
Taking the advantage of the nearly 14 000 items of muhi-source, multi-dimension practical dataset of type 2 diabetes, and a series of data mining experiments are designed to seek for important type 2 diabetes risk fac... Taking the advantage of the nearly 14 000 items of muhi-source, multi-dimension practical dataset of type 2 diabetes, and a series of data mining experiments are designed to seek for important type 2 diabetes risk factors and their relationships with blood glucose. The valuable pathological knowledge includes, the deci- sion tree is almost identical with the list of clinical diabetic risk factors; 9 items important risk factors of type 2 diabetes were found, and the relationship between the main risk factors and the blood glucose, and the feature of critical value of the risk factors were given too in this paper. These valuable results are good to the cure and macro-control type 2 diabetes. 展开更多
关键词 type 2 diabetes risk factors critical value expectation maximization（EM） algorithm C4.5 algorithm
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Forecasting Model of Agro-meteorological Disaster Grade Based on Decision Tree 认领 引用 被引量：2
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作者 司巧梅 《Meteorological and Environmental Research》 2010年第2期85-87,90,共3页
Based on the discuss of the basic concept of data mining technology and the decision tree method,combining with the data samples of wind and hailstorm disasters in some counties of Mudanjiang region,the forecasting mo... Based on the discuss of the basic concept of data mining technology and the decision tree method,combining with the data samples of wind and hailstorm disasters in some counties of Mudanjiang region,the forecasting model of agro-meteorological disaster grade was established by adopting the C4.5 classification algorithm of decision tree,which can forecast the direct economic loss degree to provide rational data mining model and obtain effective analysis results. 展开更多
关键词 Data mining Agro-meteorology Decision tree C4.5 algorithm Classification mining China
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Effective use of FibroTest to generate decision trees in hepatitis C 认领 引用 被引量：2
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作者 Dana Lau-Corona Luís Alberto Pineda +10 位作者 Héctor Hugo Avilés Gabriela Gutiérrez-Reyes Blanca Eugenia Farfan-Labonne Rafael Núez-Nateras Alan Bonder Rosalinda Martínez-García Clara Corona-Lau Marco Antonio Olivera-Martínez Maria Concepción Gutiérrez-Ruiz Guillermo Robles-Díaz David Kershenobich 《World Journal of Gastroenterology》 SCIE CAS 2009年第21期2617-2622,共6页
AIM： To assess the usefulness of FibroTest to forecast scores by constructing decision trees in patients with chronic hepatitis C.METHODS： We used the C4.5 classification algorithm to construct decision trees with d... AIM： To assess the usefulness of FibroTest to forecast scores by constructing decision trees in patients with chronic hepatitis C.METHODS： We used the C4.5 classification algorithm to construct decision trees with data from 261 patients with chronic hepatitis C without a liver biopsy. The FibroTest attributes of age, gender, bilirubin, apolipoprotein, haptoglobin, α2 macroglobulin, and γ-glutamyl transpeptidase were used as predictors, and the FibroTest score as the target. For testing, a 10-fold cross validation was used.RESULTS： The overall classification error was 14.9% （accuracy 85.1%）. FibroTest＇s cases with true scores of FO and F4 were classified with very high accuracy （18/20 for FO, 9/9 for FO-1 and 92/96 for F4） and the largest confusion centered on F3. The algorithm produced a set of compound rules out of the ten classification trees and was used to classify the 261 patients. The rules for the classification of patients in FO and F4 were effective in more than 75% of the cases in which they were tested.CONCLUSION： The recognition of clinical subgroups should help to enhance our ability to assess differences in fibrosis scores in clinical studies and improve our understanding of fibrosis progression, 展开更多
关键词 Hepatitis C FibroTest Decision trees C4.5algorithm Non-invasive biomarkers
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A New Framework for Scholarship Predictor Using a Machine Learning Approach 认领 引用
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作者 Bushra Kanwal Rana Saud Shoukat +3 位作者 Saif Ur Rehman Mahwish Kundi Tahani AlSaedi Abdulrahman Alahmadi 《Intelligent Automation & Soft Computing》 2024年第5期829-854,共26页
Education is the base of the survival and growth of any state,but due to resource scarcity,students,particularly at the university level,are forced into a difficult situation.Scholarships are the most significant fina... Education is the base of the survival and growth of any state,but due to resource scarcity,students,particularly at the university level,are forced into a difficult situation.Scholarships are the most significant financial aid mechanisms developed to overcome such obstacles and assist the students in continuing with their higher studies.In this study,the convoluted situation of scholarship eligibility criteria,including parental income,responsibilities,and academic achievements,is addressed.In an attempt to maximize the scholarship selection process,numerous machine learning algorithms,including Support Vector Machines,Neural Networks,K-Nearest Neighbors,and the C4.5 algorithm,were applied.The C4.5 algorithm,owing to its efficiency in the prediction of scholarship beneficiaries based on extraneous factors,was capable of predicting a phenomenal 95.62%of predictions using extensive data of a well-esteemed government sector university from Pakistan.This percentage is 4%and 15%better than the remainder of the methods tested,and it depicts the extent of the potential for the technique to enhance the scholarship selection process.The Decision Support Systems(DSS)would not only save the administrative cost but would also create a fair and transparent process in place.In a world where accessibility to education is the key,this research provides data-oriented consolidation to ensure that deserving students are helped and allowed to get the financial assistance that they need to reach higher studies and bridge the gap between the demands of the day and the institutions of intellect. 展开更多
关键词 Education data mining C4.5 algorithm decision support system scholarship guarantee machine learning
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基于分区决策树组合分类器的多目标分类方法 认领 引用
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作者 师永征 《电脑知识与技术》 2015年第12期14-17,共4页
针对地面有多个无规则运动的目标时，空中机器人很难判断跟踪哪一个的问题，提出一种基于分区决策树组合分类器的地面多目标分类方法。该方法对原始数据集分区处理，分别通过Bagging采样策略形成不同子数据集，利用C4．5经典算法构建基... 针对地面有多个无规则运动的目标时，空中机器人很难判断跟踪哪一个的问题，提出一种基于分区决策树组合分类器的地面多目标分类方法。该方法对原始数据集分区处理，分别通过Bagging采样策略形成不同子数据集，利用C4．5经典算法构建基分类器，并用多数投票机制对基分类器预测结果集成输出，分区构建多目标分类模型。仿真表明相对于传统的C4．5算法，该方法能够快速实现地面多运动目标分类，提高空中机器人对地面运动目标的跟踪控制率。 展开更多
关键词 多目标 分区决策树 C4.5算法 Bagging 空中机器人 组合分类器
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Improving naive Bayes classifier by dividing its decision regions 认领 引用 被引量：5
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作者 Zhi-yong YAN Gong-fu XU Yun-he PAN 《Journal of Zhejiang University-Science C(Computers and Electronics)》 2011年第8期647-657,共11页
Classification can be regarded as dividing the data space into decision regions separated by decision boundaries.In this paper we analyze decision tree algorithms and the NBTree algorithm from this perspective.Thus,a ... Classification can be regarded as dividing the data space into decision regions separated by decision boundaries.In this paper we analyze decision tree algorithms and the NBTree algorithm from this perspective.Thus,a decision tree can be regarded as a classifier tree,in which each classifier on a non-root node is trained in decision regions of the classifier on the parent node.Meanwhile,the NBTree algorithm,which generates a classifier tree with the C4.5 algorithm and the naive Bayes classifier as the root and leaf classifiers respectively,can also be regarded as training naive Bayes classifiers in decision regions of the C4.5 algorithm.We propose a second division(SD)algorithm and three soft second division(SD-soft)algorithms to train classifiers in decision regions of the naive Bayes classifier.These four novel algorithms all generate two-level classifier trees with the naive Bayes classifier as root classifiers.The SD and three SD-soft algorithms can make good use of both the information contained in instances near decision boundaries,and those that may be ignored by the naive Bayes classifier.Finally,we conduct experiments on 30 data sets from the UC Irvine(UCI)repository.Experiment results show that the SD algorithm can obtain better generali-zation abilities than the NBTree and the averaged one-dependence estimators(AODE)algorithms when using the C4.5 algorithm and support vector machine(SVM)as leaf classifiers.Further experiments indicate that our three SD-soft algorithms can achieve better generalization abilities than the SD algorithm when argument values are selected appropriately. 展开更多
关键词 Naive Bayes classifier Decision region NBTree C4.5 algorithm Support vector machine(SVM)
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