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.

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.

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.

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,

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。

基于聚类与决策树的综合入侵检测算法研究

入侵检测是一种通过实时监测目标系统来发现入侵攻击行为的安全技术,传统的入侵检测系统在有效性、适应性和可扩展性方面都存在着不足。为了使模糊聚类算法获得的聚类结果为全局最优解,改进了传统的模糊C-均值算法,并且在每个聚类的数据集上建立一棵属于该聚类的C4.5决策树,构造了一种新的综合检测算法来确定是否存在入侵。通过实验结果分析,该检测算法降低了误报率,提高了入侵检测的检测性能以及可靠性。

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。

Study on the Grouping of Patients with Chronic Infectious Diseases Based on Data Mining

Objective: According to RFM model theory of customer relationship management, data mining technology was used to group the chronic infectious disease patients to explore the effect of customer segmentation on the management of patients with different characteristics. Methods: 170,246 outpatient data was extracted from the hospital management information system (HIS) during January 2016 to July 2016, 43,448 data was formed after the data cleaning. K-Means clustering algorithm was used to classify patients with chronic infectious diseases, and then C5.0 decision tree algorithm was used to predict the situation of patients with chronic infectious diseases. Results: Male patients accounted for 58.7%, patients living in Shanghai accounted for 85.6%. The average age of patients is 45.88 years old, the high incidence age is 25 to 65 years old. Patients was gathered into three categories: 1) Clusters 1—Important patients (4786 people, 11.72%, R = 2.89, F = 11.72, M = 84,302.95);2) Clustering 2—Major patients (23,103, 53.2%, R = 5.22, F = 3.45, M = 9146.39);3) Cluster 3—Potential patients (15,559 people, 35.8%, R = 19.77, F = 1.55, M = 1739.09). C5.0 decision tree algorithm was used to predict the treatment situation of patients with chronic infectious diseases, the final treatment time (weeks) is an important predictor, the accuracy rate is 99.94% verified by the confusion model. Conclusion: Medical institutions should strengthen the adherence education for patients with chronic infectious diseases, establish the chronic infectious diseases and customer relationship management database, take the initiative to help them improve treatment adherence. Chinese governments at all levels should speed up the construction of hospital information, establish the chronic infectious disease database, strengthen the blocking of mother-to-child transmission, to effectively curb chronic infectious diseases, reduce disease burden and mortality.

C 5.0决策树对早期胃癌风险筛查研究

目的 C 5.0算法改进C 4.5算法以提高分类效率和准确性,越来越广泛地应用于处理分类问题。本研究拟根据患者问卷调查和血清学检查等资料,利用C 5.0决策树算法筛查早期胃癌风险,筛选对早期胃癌风险筛查影响较大的因素,进而辅助临床提高早期胃癌的诊断筛查。方法资料来自与广东药科大学附属第一医院的合作项目"基于云计算的早期胃癌筛查创新平台",对广东省6个市近30家医院消化内科就诊的618例胃病患者进行问卷调查,并收集其血清学检查和内镜检查及病理活组织检查资料。根据内镜检查和病理活组织检查结果将患者分为早期胃癌低危、中危及高危3类,用合成少数过采样技术(synthetic minority oversampling technique,SMOTE)方法处理样本分类不平衡问题,然后根据C 5.0算法建立早期胃癌风险筛查的决策树模型。结果产生1棵深度为11、共33个叶子节点的C 5.0决策树模型,对应有33条易于理解的分类规则,根据这些分类规则可快速评估患者的早期胃癌风险类型。建立的C 5.0决策树模型有较高的准确率,达73.28%,且增益图中曲线上凸明显,接近理想曲线,能较好地对早期胃癌风险进行分类预测。决策树模型计算各指标对早期胃癌风险预测的重要性,筛选出15个对早期胃癌风险筛查影响较大的因素,其中影响最大的因素是幽门螺旋杆菌(helicobacter pylori,Hp)抗体。结论基于患者问卷调查和血清学检查构建的C 5.0决策树模型对早期胃癌风险的预测效果较好,选出对早期胃癌风险筛查影响较大的因素,可辅助临床早期胃癌风险筛查。