Research on Scholarship Evaluation System based on Decision Tree Algorithm

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.

C4.5算法的优化

对传统C4.5算法的运算效率和属性选择准确性进行研究,对其进行改进。运用泰勒级数和等价无穷小的原理对算法的计算公式进行简化,提高运算效率;在简化后的信息增益率计算公式中引入其它非类属性对于该属性的GINI指数的均值,用于调整因非类属性间冗余度问题导致的误差,提高算法属性选择的准确性,将改进后的算法称为G_C4.5。对G_C4.5、传统C4.5算法与其它改进算法进行对比实验分析,分析结果表明,G_C4.5算法在分类效率和准确性上都有一定提高。

Application Comparison of Association Rules and C4.5 Rules in Land Evaluation

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.

Forecasting Model of Agro-meteorological Disaster Grade Based on Decision Tree

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.

C4.5数据挖掘算法的改进及其应用

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。

面向乳腺肿瘤的诊前问答系统决策模型构建研究

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。

基于局部择优离散技术的C4.5改进算法及其在学生成绩评价中的应用

针对高校教务管理系统中学生成绩数据连续值偏多的情况,导致无法对学生成绩有效地进行智能分析等问题,提出了基于局部择优离散技术的C4.5改进算法,进而构建学生成绩分析模型,并采用后剪枝算法对模型进行了优化,抽取了学生成绩的分类规则。实验表明,改进后的C4.5算法保证较高分类正确率的同时,执行效率得到了提高,有助于挖掘出学生成绩与各种因素之间的潜在联系,为教学工作改革提供决策依据和支持。

Land Evaluation Method Based on Decision Tree Produced by C4.5 and Fuzzy Decision

[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.

Effective use of FibroTest to generate decision trees in hepatitis C

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,

Some Pathological Knowledge Discovered in Large Database of Type 2 Diabetes

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.

A New Framework for Scholarship Predictor Using a Machine Learning Approach

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.

Improving naive Bayes classifier by dividing its decision regions

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.