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.

A New Speculative Execution Algorithm Based on C4.5 Decision Tree for Hadoop

As a distributed computing platform, Hadoop provides an effective way to handle big data. In Hadoop, the completion time of job will be delayed by a straggler. Although the definitive cause of the straggler is hard to detect, speculative execution is usually used for dealing with this problem, by simply backing up those stragglers on alternative nodes. In this paper, we design a new Speculative Execution algorithm based on C4.5 Decision Tree, SECDT, for Hadoop. In SECDT, we speculate completion time of stragglers and also of backup tasks, based on a kind of decision tree method: C4.5 decision tree. After we speculate the completion time, we compare the completion time of stragglers and of the backup tasks, calculating their differential value, and selecting the straggler with the maximum differential value to start the backup task.Experiment result shows that the SECDT can predict execution time more accurately than other speculative execution methods, hence reduce the job completion time.

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.

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.

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,

A decision tree based decomposition method for oil refinery scheduling

Refinery scheduling attracts increasing concerns in both academic and industrial communities in recent years.However, due to the complexity of refinery processes, little has been reported for success use in real world refineries. In academic studies, refinery scheduling is usually treated as an integrated, large-scale optimization problem,though such complex optimization problems are extremely difficult to solve. In this paper, we proposed a way to exploit the prior knowledge existing in refineries, and developed a decision making system to guide the scheduling process. For a real world fuel oil oriented refinery, ten adjusting process scales are predetermined. A C4.5 decision tree works based on the finished oil demand plan to classify the corresponding category(i.e. adjusting scale). Then,a specific sub-scheduling problem with respect to the determined adjusting scale is solved. The proposed strategy is demonstrated with a scheduling case originated from a real world refinery.

FPGA-Based Network Traffic Security: Design and Implementation Using C5.0 Decision Tree Classifier

In this work, a hardware intrusion detection system （IDS） model and its implementation are introduced to perform online real-time traffic monitoring and analysis. The introduced system gathers some advantages of many IDSs： hardware based from implementation point of view, network based from system type point of view, and anomaly detection from detection approach point of view. In addition, it can detect most of network attacks, such as denial of services （DOS）, leakage, etc. from detection behavior point of view and can detect both internal and external intruders from intruder type point of view. Gathering these features in one IDS system gives lots of strengths and advantages of the work. The system is implemented by using field programmable gate array （FPGA）, giving a more advantages to the system. A C5.0 decision tree classifier is used as inference engine to the system and gives a high detection ratio of 99.93%.

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.

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.

An Active Rule Approach for Network Intrusion Detection with Enhanced C4.5 Algorithm

Intrusion detection systems provide additional defense capacity to a networked information system in addition to the security measures provided by the firewalls. This paper proposes an active rule based enhancement to the C4.5 algorithm for network intrusion detection in order to detect misuse behaviors of internal attackers through effective classification and decision making in computer networks. This enhanced C4.5 algorithm derives a set of classification rules from network audit data and then the generated rules are used to detect network intrusions in a real-time environment. Unlike most existing decision tree based approaches, the spawned rules generated and fired in this work are more effective because the information-theoretic approach minimizes the expected number of tests needed to classify an object and guarantees that a simple (but not necessarily the simplest) tree is found. The main advantage of this proposed algorithm is that the generalization ability of enhanced C4.5 decision trees is better than that of C4.5 decision trees. We have employed data from the third international knowledge discovery and data mining tools competition (KDDcup’99) to train and test the feasibility of this proposed model. By applying the enhanced C4.5 algorithm an average detection rate of 93.28 percent and a false positive rate of 0.7 percent have respectively been obtained in this work.

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。

基于模糊C均值聚类和梯度提升决策树的护林员评价方法

现有关于基层护林员科学、客观、精准的评价方法的研究十分缺乏,传统的人员绩效评价方法也不适用于护林员巡护情况的评价。本文以中国东南部某县级市的护林员为研究对象,自创1种基于模糊C均值聚类(Fuzzy C-means,FCM)结果和FCM隶属度以及梯度提升决策树相结合的护林员巡护情况评价方法。首先对护林员巡护情况数据集进行Z-Score标准化处理以提高算法的准确率和效率,其次以里程数、考勤率、耗时数和上报事件数为特征变量,使用FCM对巡护情况数据集进行聚类,确定基准月,并使用隶属度评价得分划定法计算基准月护林员评价得分,再通过梯度提升决策树(Gradient boosting decision tree,GBDT)和基准月数据确定研究期内其他月份的护林员评价得分,最后对护林员巡护情况进行综合分析。研究结果表明,该方法可精准、清晰地划定护林员巡护情况评价得分;研究期内护林员整体巡护情况偏差,评价得分≤60分的人数占比较大;常驻护林员在研究期内巡护情况评价得分基本保持不变,偶尔上下波动,毫无提升。本文的方法从实际数据出发,对护林员巡护情况进行针对性的分析,使得护林员管理者可制定科学的管理方案,并以期为护林员巡护情况的评价方法提供新的方向和思路。

Taiga: Performance Optimization of the C4.5 Decision Tree Construction Algorithm

Classification is an important machine learning problem, and decision tree construction algorithms are an important class of solutions to this problem. RainForest is a scalable way to implement decision tree construction algorithms. It consists of several algorithms, of which the best one is a hybrid between a traditional recursive implementation and an iterative implementation which uses more memory but involves less write operations. We propose an optimized algorithm inspired by RainForest. By using a more sophisticated switching criterion between the two algorithms, we are able to get a performance gain even when all statistical information fits in memory. Evaluations show that our method can achieve a performance boost of 2.8 times in average than the traditional recursive implementation.

Data mining and well logging interpretation: application to a conglomerate reservoir

Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We used data mining to identify the lithologies in a complex reservoir. The reservoir lithologies served as the classification task target and were identified using feature extraction, feature selection, and modeling of data streams. We used independent component analysis to extract information from well curves. We then used the branch-and- bound algorithm to look for the optimal feature subsets and eliminate redundant information. Finally, we used the C5.0 decision-tree algorithm to set up disaggregated models of the well logging curves. The modeling and actual logging data were in good agreement, showing the usefulness of data mining methods in complex reservoirs.

基于C&R决策树的茶饮料用原料茶初筛方法

对于茶饮料而言,原料茶质量是决定最终产品品质的重要基础。目前对茶饮料用原料茶的筛选,往往需要将其制成产品后进行样本质量的评价,每种原料茶的评价都需要经过茶汤制作、灭菌、感官评价(包括灭菌前、灭菌后、贮藏期等)等一系列复杂过程。在进行大规模样本筛选时,此评价需花费大量的时间和精力。为了提升茶饮料用原料茶的筛选效率,本研究分析了不同原料茶制作茶饮料过程中,各阶段的汤色以及与汤色相关的明亮度L、色差-a、b、-a/b值和浊度等指标的变化规律,并结合C&R决策树研究一种利用汤色色度指标针对茶饮料用原料茶的快速初筛方法。利用该方法进行初筛时可将原料茶的筛选范围从62个缩减到44个,减少了29.0%,筛选效率从0.226提高到0.318,提高了40.7%,并且得出灭菌前-a/b>0.475可作为茶饮料用原料茶的一个主要筛选标准。本研究结果能为饮料用原料茶快速筛选方法的建立提供参考。

基于决策树的矿产资源潜力制图模型

文中提出了一种基于决策树的矿产资源潜力制图模型.应用该模型生成矿产资源潜力分布图分三步完成:第一步,以找矿标志的空间分布图和已知矿点空间分布图为依据,提取训练样本;第二步,根据训练样本构建决策树矿产资源潜力制图模型;第三步,生成矿产资源潜力分布图.本文以新疆北部阿尔泰多金属成矿带为研究区,比较了该模型与合成有矿可信度等模型的找矿靶区圈定结果.两种模型的靶区圈定结果基本相同,证明了决策树矿产资源潜力制图模型的有效性.

复杂景观环境下土壤厚度分布规则提取与制图

复杂景观环境下,土壤—环境关系知识的获取是预测性土壤制图的基础。为了探究复杂景观下土壤厚度分布与环境条件的关系,该文以黑河上游祁连山区典型小流域为研究区,应用模糊c均值聚类(fuzzy C-means cluster,FCM)和决策树(decision Tree,DT)方法,建立了一套获取土壤厚度分布与环境间关系知识的方法。利用2种方法结合获得流域内土壤厚度各分布等级的环境要素关键阈值与土壤-环境关系知识集,将所得环境阈值和知识集进行预测性制图,并通过野外独立样点对制图结果进行精度评价。结果表明:土壤厚度图的总体精度为74.2%,Kappa系数为0.659。该研究将2种方法结合获得了土壤厚度分布对应的土壤环境关键阈值和土壤-环境关系知识集,为复杂景观环境下土壤厚度的预测性制图提供了一种有效的解决方案。

一种新的支持向量机决策树设计算法

支持向量机决策树的精度和速度取决于树结构。为了获得好的泛化性能,应由可分性强的类为树的上层结点定义分类子任务。提出了一种新的支持向量机决策树设计算法。决策树中每个结点的分类子任务定义规则如下:采用模糊核C-均值将当前训练集粗分为两个子集,然后基于隶属度从各个子集中选择可分性强的子类定义当前结点的分类子任务,并将可分性弱的子类移至下层结点。实验结果表明,该方法的精度和速度都优于其他传统的多类分类方法。

基于不平衡数据的公司破产预测研究

整合创新数据预处理技术与集成算法利用不平衡数据探讨了公司破产预测问题。首先,运用冗余信息处理方法、不同抽样方法等对不平衡数据进行预处理。其次,以5.0分类器(Classifier 5.0,C5.0)决策树和单隐层前馈神经网络作为基分类器,分别与三类重抽样数据预处理技术结合,择出最优抽样法。再次,结合自助汇聚法提升分类效果,并运用十折交叉验证的受试者操作特征曲线的下方面积进行评价,对比了两基分类器的集成模型。最后,运用加利福尼亚大学尔湾分校数据库中一万多家波兰制造业公司的实际数据进行实验验证。实验结果表明:欠抽样或人工少数类过采样法与神经网络结合的集成模型分类效果最优,为企业实施破产预测提供积极支撑。

基于决策树的早稻管理专家系统的设计与实现

近年来,农业专家系统的迅速发展为水稻生产管理的现代化和信息化提供了新的方法和手段,并在生产中得到了广泛的推广和应用。本文通过使用产生式表示知识规则,并基于决策树的方法进行推理,利用C++Builder实现一个早稻管理专家系统。