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2nd International Workshop on Knowledge Discovery Data Mining

作者： Niu, Zhixian Zong, Lili Yan, Qingwei Zhao, Zhenxing Taiyuan Univ Technol Coll Comp & Software Taiyuan 030024 Shanxi Peoples R China

ISBN: (纸本)9780769535432

The type of the workload is one of the key factors on database management system (DBMS) tuning. Different types of workload (OLTP, Online Transaction Processing and OLAP, Online Analytical Processing) mean different resource allocation strategies. In this paper, we present an approach to automatically identify a DBMS workload as either OLTP or OLAP. We use c5.0 algorithm to construct a set of classifiers based on the characteristics that differentiate OLTP and OLAP and then use the classifier to identify the workload type. The experiments show that the classifiers can be able to accurately identify the OLTP and OLAP workloads.

关键词： c5.0 algorithm OLTP OLAP classifier self-managed DBMS

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Auto-Recognizing DBMS Workload Based on c5.0 algorithm

Auto-Recognizing DBMS Workload Based on C5.0 Algorithm

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2009 Second International Workshop on Knowledge Discovery and Data Mining

作者： Zhixian Niu,Lili Zong,Qingwei Yan,Zhenxing Zhao college of computer and Software, Taiyuan University of Technology,TYUT Taiyuan,ShanXi,030024,china

The type of the workload is one of the key factors on database management system(DBMS) tuning. Different types of workload(OLTP,Online Transaction Processing and OLAP,Online Analytical Processing) mean different resource allocation *** this paper,we present an approach to automatically identify a DBMS workload as either OLTP or *** use c5.0 algorithm to construct a set of classifiers based on the characteristics that differentiate OLTP and OLAP and then use the classifier to identify the workload *** experiments show that the classifiers can be able to accurately identify the OLTP and OLAP workloads.

关键词： c5.0 algorithm OLTP OLAP classifier self-managed DBMS

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c5.0 classification algorithm and Application on Individual credit Evaluation of Banks

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Systems Engineering - Theory & Practice 2009年第12期29卷 94-104页

作者： Su-lin PANG Ji-zhang GONG Institute of Finance Engineering School of Management Jinan University Guangzhou 510632

This article focuses on individual credit evaluation of commercial bank. The records of individual credit include both numerical and nonnumeric data. Decision tree is a good solution for this kind of issue. This year, the algorithm c4.5 of decision tree become popular, but c5.0 algorithm is still undergoing. In this article, we do some deep research on c5.0 algorithm by embedding “boosting” technology in cost matrix and cost-sensitive tree to establish a new model for individual credit evaluation of commercial Bank. We apply our new model on evaluating the individual credit records of a German bank, and compared results of the adjusted decision tree model and the original one. The comparison shows that the adjusted decision tree model is more precise.

关键词： credit evaluation decision tree c5.0 algorithm boosting technology cost-sensitive tree

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Metaheuristic Firefly and c5.0 algorithms Based Intrusion Detection for critical Infrastructures 3

Metaheuristic Firefly and C5.0 Algorithms Based Intrusion De...

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3rd International conference on Applied Artificial Intelligence (IcAPAI) - Human-centric AI for the Digital Society

作者： Adeyiola, Afolabi Qudus Saheed, Yakub Kayode Misra, Sanjay chockalingam, Sabarathinam Al Hikmah Univ Dept Comp Sci Ilorin Nigeria Ostfold Univ Coll Dept Comp Sci & Commun Halden Norway Inst Energy Technol Dept Risk & Secur Halden Norway

ISBN: (纸本)9798350328929

Wireless Sensor Networks (WSN) are groups of stand-alone gadgets that typically feature one or more sensors (for example, light level, temperature), with relatively limited computing capabilities, and a wireless connection that enable interaction with a base station. Today, WSN is being implemented within critical infrastructures such as connected vehicles, drones, smart cities, smart grids, and surveillance systems. The major issue of WSN is that they are primarily focused on security issues linked to packet transfer across network's multiple sensor nodes. Intrusion detection is essential due to the growing importance of WSN security. To address this flaw in WSN, an effective wrapper feature selection founded on the Firefly algorithm (FFA) is developed for the selection of significant attributes in this paper. This wrapper-based feature selection solution reduces time consumption to a higher extent while also increasing the network's lifetime and scalability. In the first phase of this work, data preprocessing was performed with a minimum maximum normalization approach, subsequently, FFA was used for feature dimensionality reduction and c5.0 for the classification. The simulations were done using the UNSW-NB15 benchmark data, and the suggested firefly with c5.0 (FFA-05.0) has an accuracy of 98.7%.

关键词： Wireless Sensor Networks Firefly algorithm Intrusion Detection c5.0 algorithm Minimum Maximum Normalization UNSW-NB15

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Analysis of Higher Education Teaching Data Based on Data Mining Technology 24

Analysis of Higher Education Teaching Data Based on Data Min...

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Proceedings of the 2024 2nd International conference on Information Education and Artificial Intelligence

作者： Zhiqi Wang Criminal Investigation Police University of China Shenyang Liaoning China

ISBN: (纸本)9798400711732

To efficiently extract and leverage the vast amounts of data stored within university academic management systems, a novel approach utilizing data mining techniques for analyzing higher education teaching data has been proposed. This study delves into the performance data recorded in the academic affairs system, aiming to uncover meaningful insights. Initially, data collection and preprocessing are conducted to ensure clean and structured input. Subsequently, a comprehensive evaluation of student performance is performed using factor analysis. For predictive purposes, an enhanced decision tree model is developed by integrating the K-means clustering algorithm with the c5.0 algorithm. This hybrid approach is benchmarked against alternative methodologies to assess its effectiveness. Experimental results demonstrate that the enhanced decision tree achieves the highest prediction accuracy of 64.8%, characterized by minimal tree depth and a reduced number of leaf nodes. This not only enhances model precision but also ensures greater robustness while mitigating the risk of overfitting. These findings highlight the superiority of the proposed method in analyzing and predicting academic performance within the context of higher education.

关键词： c5.0 algorithm

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Pattern identification and risk prediction of domino effect based on data mining methods for accidents occurred in the tank farm

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2020年 193卷 106646-000页

作者： Hou, Lei Wu, Xingguang Wu, Zhuang Wu, Shouzhi China Univ Petr Beijing Key Lab Urban Oil & Gas Disirthut Technol Beijing 102249 Peoples R China China Univ Petr MOE Key Lab Petr Engn Beijing 102249 Peoples R China China Univ Petr Natl Engn Lab Pipeline Safety Beijing 102249 Peoples R China

In recent decades, scarce work was done on risk management of the domino effect by establishing the relationship models between influencing factors and consequences of accidents. In this study, the statistical analysis of the 1144 accidents of tank farms including 100 domino accidents and 1044 non-domino accidents occurred in china from 1960 to 2018 was performed. Unlike the existing statistical analysis literature, the causes of the primary events and the secondary events were separately analyzed to determine the common causes of accident escalation. The c5.0 decision tree algorithm was adopted to extract rules that show the most likely sequences of causal factors for triggering domino accidents. Association rule mining was performed on season factors, units of accidents, operation status and causal factors to predict the causal factors under different process conditions and scenarios. The results showed that inadequate training, inadequate procedure and design deficiency were the most important factors in the c5.0 models on the prediction of domino risks. Six decision rules were learned by c5.0 algorithm and twenty effective rules were learned by the association rule mining, which can jointly provide accurate and reliable prevention strategies and decision support for the risk management of domino accidents.

关键词： Statistical analysis c5.0 algorithm Association rule Domino effect Tank farm

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combining random forest and support vector machines for object-based rural-land-cover classification using high spatial resolution imagery

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JOURNAL OF APPLIED REMOTE SENSING 2019年第1期13卷

作者： Xu, Saiping Zhao, Qianjun Yin, Kai Zhang, Feifei Liu, Dingbang Yang, Guang Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

Land-cover classification using remote sensing imagery is an important part of environmental research because it provides baseline information for ecological vulnerability and risk assessment, disaster management, landscape conservation, local and regional planning, and so on. Rural-land-cover classification is challenging for both object-based image analysis methods and classifiers. The objective of this study is to improve the object-oriented classification accuracy of rural land cover by combining two models based on high spatial resolution imagery. We apply the c5.0 algorithm in R to combine support vector machines (SVMs) and random forest (RF) to create the model RS_c5.0. The effectiveness of the model combination is assessed by comparing the classification results with the state-of-the-art machine learning algorithm, namely extreme gradient boosting (XGBoost). The comparisons are done based on the classification results of both the study area and the case area. Results show that in the classification of the study area, RF performs slightly better than SVM, and XGBoost performs worse than RF but better than SVM. However, in the classification of the case area, SVM performs slightly better than RF and both SVM and RF perform better than XGBoost. Furthermore, RS_c5.0 obtains the highest overall accuracies and kappa coefficients in the classifications of both the study area and the case area. In terms of training time, XGBoost runs the slowest in the classifications of both the study area and the case area. SVM and RF as well as the combined model (RS_c5.0) run much faster than XGBoost classifier. To summarize, the combination of SVM and RF classifiers using c5.0 algorithm is found to be a fast and effective way to improve rural-land-cover classification. (c) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： random forest support vector machines c5.0 algorithm XGBoost algorithm object-based image analysis

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Performance analysis of classification algorithms on early detection of liver disease

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EXPERT SYSTEMS WITH APPLIcATIONS 2017年第0期67卷 239-251页

作者： Abdar, Moloud Zomorodi-Moghadam, Mariam Das, Resul Ting, I-Hsien Damghan Univ Dept Comp Engn Damghan Iran Ferdowsi Univ Mashhad Dept Comp Engn Mashhad Iran Firat Univ Fac Technol Dept Software Engn Elazig Turkey Natl Univ Kaohsiung Dept Informat Management Kaohsiung Taiwan

The human liver is one of the major organs in the body and liver disease can cause many problems in human life. Fast and accurate prediction of liver disease allows early and effective treatments. In this regard, various data mining techniques help in better prediction of this disease. Because of the importance of liver disease and increase the number of people who suffer from this disease, we studied on liver disease through using two well-known methods in data mining area. In this paper, novel decision tree based algorithms is used which leads to considering more factors in general and predictions with high accuracy compared to other studies in liver disease. In this application, 583 UcI instances of liver disease dataset from the UcI repository are considered. This dataset consists of 416 records of liver disease and 167 records of healthy liver. This dataset is analyzed by two algorithms named Boosted c5.0 and cHAID algorithms. Until now there is no work in the literature that uses boosted c5.0 and cHAID for creating the rules in liver disease. Our results show that in both algorithms, the DB, ALB, SGPT, TB and A/G factors have a significant impact on predicting liver disease which according to the rules generated by both algorithms important ranges are DB = [10.900-1.200], ALB [4.00-4.300], SGPT=[34-37], TB=[0.600-1.200] (by boosted c5.0), A/G=[1.180-1.390],as well as in the Boosted c5.0 algorithm, Alkphos, SGOT and Age have significant impact in prediction of liver disease. By comparing the performance of these algorithms, it becomes clear that c5.0 algorithm via Boosting technique has an accuracy of 93.75% and this result reveals that it has a better performance than the cHAID algorithm which is 65.00%. Another important achievement of this paper is about the ability of both algorithms to produce rules in one class for liver disease. The results of our assessment show that Boosted c5.0 and cHAID algorithms are capable to produce rules for liver disease. Our r

关键词： Liver disease c5.0 algorithm Boosting technique cHAID algorithm Data mining classification

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Types of problematic smartphone use based on psychiatric symptoms

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PSYcHIATRY RESEARcH 2019年 275卷 46-52页

作者： Rho, Mi Jung Park, Jihwan Na, Euihyeon Jeong, Jo-Eun Kim, Jae Kwon Kim, Dai-Jin choi, In Young Catholic Univ Korea Catholic Canc Res Inst Seoul South Korea Incheon Chamsarang Hosp Dept Psychiat Seoul South Korea Catholic Univ Korea Coll Med Daejeon St Marys Hosp Dept Psychiat Daejeon South Korea Inha Univ Dept Comp Sci & Informat Engn Incheon South Korea Catholic Univ Korea Coll Med Seoul St Marys Hosp Dept Psychiat Seoul South Korea Catholic Univ Korea Coll Med Seoul St Marys Hosp Addict Res InstDept Psychiat Seoul South Korea Catholic Univ Korea Coll Med Dept Med Informat Seoul South Korea Catholic Univ Korea Coll Med Dept Biomed & Hlth Sci Seoul South Korea

To provide appropriate solutions for problematic smartphone use, we need to first understand its types. This study aimed to identify types of problematic smartphone use based on psychiatric symptoms, using the decision tree method. We recruited 5,372 smartphone users from online surveys conducted between February 3 and February 22, 2016. Based on scores on the Korean Smartphone Addiction Proneness Scale for Adults (S-Scale), 974 smartphone users were assigned to the smartphone-dependent group and 4398 users were assigned to the normal group. The data-mining technique of c5.0 decision tree was applied. We used 15 input variables, including demographic and psychological factors. Four psychiatric variables emerged as the most important predictors: self-control (Sc;66%), anxiety (Anx;25%), depression (Dep;7%), and dysfunctional impulsivities (Imp;3%). We identified the following five types of problematic smartphone use: (1) non-comorbid, (2) self-control, (3) Sc + Anx, (4) Sc + Anx + Dep, and (5) Sc + Anx + Dep + Imp. We found that 74% of smartphone-dependent users had psychiatric symptoms. The ratio of participants belonging to the non-comorbid and self-control types was 64%. We proposed that these types of problematic smartphone use may be used for the development of an appropriate service for controlling and preventing such behaviors in adults.

关键词： Korean Smartphone Addiction Proneness Scale for Adults Decision tree analysis c5.0 algorithm Dickman Impulsivity Inventory-short version Brief Self-control Scale Generalized anxiety disorder GAD-7 scale Patient Health Questionnaire-9

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Novel machine-learning prediction tools for overall survival of patients with chondrosarcoma: Based on recursive partitioning analysis

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cANcER MEDIcINE 2024年第15期13卷 e70058页

作者： Yang, Xiong-Gang Yang, Shan-Shan Bao, Yi Wang, Qi-Yang Peng, Zhi Lu, Sheng Kunming Univ Sci & Technol Peoples Hosp Yunnan Prov 1 Affiliated Hosp Dept Orthoped Kunming Yunnan Peoples R China Key Lab Digital Orthoped Yunnan Prov Kunming Yunnan Peoples R China Zunyi Med Univ Affiliated Stomatol Hosp Dept Prosthodont Zunyi Peoples R China

Backgroundchondrosarcoma (cHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with *** from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed, including demographic, clinical, and treatment details of patients diagnosed between 2000 and 2018. Using c5.0 algorithm, decision trees were created to predict survival probabilities at 12, 24, 60, and 120 months. The performance of the models was assessed through confusion scatter plot, accuracy rate, receiver operator characteristic (ROc) curve, and area under ROc curve (AUc).ResultsThe study identified tumor histology, surgery, age, visceral (brain/liver/lung) metastasis, chemotherapy, tumor grade, and sex as critical predictors. Decision trees revealed distinct patterns for survival prediction at each time point. The models showed high accuracy (82.40%-89.09% in training group, and 82.16%-88.74% in test group) and discriminatory power (AUc: 0.806-0.894 in training group, and 0.808-0.882 in test group) in both training and testing datasets. An interactive web-based shiny APP (URL: ) was developed, simplifying the survival prediction process for *** study successfully employed RPA to develop a user-friendly tool for personalized survival predictions in cHS. The decision tree models demonstrated robust predictive capabilities, with the interactive application facilitating clinical decision-making. Future prospective studies are recommended to validate these findings and further refine the predictive model.

关键词： c5.0 algorithm chondrosarcoma decision tree model overall survival recursive partitioning analysis

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