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International conference on computer Science and Network Technology

作者： Xuefei Wang Yan Shi The Open University of Harbin

ISBN: (纸本)9781467381741

With the development of Big data, cloud storage and data mining, targeting advertising technology has aroused wide attention. So many companies have invested in the target mobile advertising market in this area. Targeting advertising can effectively save resources, reduce capital investment. Aiming at this problem, this paper proposed a new method for targeting advertising based on c4.5 algorithm and cloud storage. The mess data about users, such as user information, user browsing behavior and so on, is stored in the cloud. With c4.5 algorithm, we do mess data processing for data classification. Then, with the results of classification, the server-push blocks push targeted advertising for users. This paper introduces the structure of the system and the detailed analysis of the classification algorithm. And the classification results obtained in this paper are compared with those by other algorithms. It is believed that this paper will be helpful in the development of targeting advertising.

关键词： target advertising Big data c4.5 algorithm big data Advertising as Topic targeting Sorting algorithm data classification pushing cloud computing paper consumers

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Application of c4.5 algorithm in Evaluation of Railway Large Maintenance Machinery construction

Application of C4.5 Algorithm in Evaluation of Railway Large...

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4th International conference on Frontiers of Manufacturing and Design Science (IcFMD 2013)

作者： Zhu, Weiqiao Shi, Tianyun China Acad Railway Sci Inst Comp Technol Beijing 100081 Peoples R China

ISBN: (纸本)9783037859926

With improvement of the level of modernization and mechanization of railway infrastructure maintenance, Railway large maintenance machinery (RLMM) is considered to be one of the main equipment. We have accumulated large amounts of RLMM construction data. How to mine valuable information from these data has become an important research subject. The c4.5 algorithm of decision tree is a useful method of data mining and classification. The paper solves evaluation of RLMM construction problem based on the c4.5 algorithm. By means of extracting the attribute with maximum gain ratio as the root node of the decision tree from training sample data, the evaluation decision tree was constructed. The decision tree modeling of evaluation of RLMM construction was gained by the post-pruning approach. The experimental results and analysis show that this model has high precision and credibility.

关键词： Evaluation Railway Large Maintenance Machinery (RLMM) c4.5 algorithm Decision Tree

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The Application and Research of c4.5 algorithm

The Application and Research of C4.5 Algorithm

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International conference on Advances in Materials Science and Information Technologies in Industry (AMSITI)

作者： Zhao, Hongyan Shandong Yingcai Univ Sch Comp & Informat Engn Jinan Peoples R China

ISBN: (纸本)9783038350125

In the sustainable development of private universities, stable teachers team is the guarantee of talent training. For colleges or universities, it has important significance to analysis the main factors which cause teachers' loss. c4.5 algorithm is a kind of machine learning algorithm in the classification decision tree algorithm. Decision tree is the main technology of data mining classification and prediction. Through the survey data from the information, it establishes the teacher turnover tendency decision tree model using c4.5 algorithm to provide the decision-making basis for the university management work.

关键词： private universities teachers' loss c4.5 algorithm

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An Online Software for Decision Tree classification and Visualization using c4.5 algorithm (ODTc)

An Online Software for Decision Tree Classification and Visu...

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8th International conference on computing for Sustainable Global Development (INDIAcom)

作者： Das, Suvajit Dahiya, Shashi Bharadwaj, Anshu IARI New Delhi India IASRI New Delhi India

ISBN: (纸本)9789380544120

classification is an important and widely carried out task of data mining. It is a predictive modelling task which is defined as building a model for the target variable as a function of the explanatory variables. There are many well established techniques for classification, while decision tree is a very important and popular technique from the machine learning domain. Decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs and utility. c4.5 is a well known decision tree algorithm used for classifying datasets. The c4.5 algorithm is Quinlan's extension of his own ID3 algorithm for decision tree classification. It induces decision trees and generates rules from datasets, which could contain categorical and/or numerical attributes. The rules could he used to predict categorical values of attributes from new records. c4.5 performs well in classifying the dataset as well as in generating useful rules. In this paper, a web based software for rule generation and decision tree induction using c4.5 algorithm has been discussed. The visualization in the form of tree structure enhances the understanding of the generated rules. The software contains the feature to impute the missing values in data. The input data can both he categorical and numerical in nature. The software can import TXT, XLS and cSV data file formats. Enhanced waterfall model has been used for the software development process. This software will he useful for academicians, researchers and students working in the area of data mining, agriculture and other fields where huge amount of data is generated.

关键词： c4.5 algorithm classification Data mining Decision Tree waterfall model

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Features of Detecting Malicious Installation Files Using Machine Learning algorithms

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AUTOMATIc cONTROL AND cOMPUTER ScIENcES 2023年第8期57卷 968-974页

作者： Yugai, P. E. Zhukovskii, E. V. Semenov, P. O. Peter Great St Petersburg Polytech Univ St Petersburg 195251 Russia

This paper presents a study of the possibility of using machine learning methods to detect malicious installation files related to the type of Trojan installers and downloaders. A comparative analysis of machine learning algorithms applicable for the solution of this problem is provided: the naive Bayes classifier (NBc), random forest, and c4.5 algorithm. Machine learning models are developed using the Weka software. The most significant attributes of installation files of legitimate and Trojan programs are highlighted.

关键词： malware installation files Trojans machine learning naive Bayes classifier random forest c4.5 algorithm

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Optimizing the c4.5 Decision Tree algorithm using MSD-Splitting

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INTERNATIONAL JOURNAL OF ADVANcED cOMPUTER ScIENcE AND APPLIcATIONS 2020年第10期11卷 41-47页

作者： Rim, Patrick Liu, Erin CALTECH Pasadena CA 91125 USA Troy High Sch Fullerton CA 92831 USA

We propose an optimization of Dr. Ross Quinlan's c4.5 decision tree algorithm, used for data mining and classification. We will show that by discretizing and binning a data set's continuous attributes into four groups using our novel technique called MSD-Splitting, we can significantly improve both the algorithm's accuracy and efficiency, especially when applied to large data sets. We applied both the standard c4.5 algorithm and our optimized c4.5 algorithm to two data sets obtained from Uc Irvine's Machine Learning Repository: census Income and Heart Disease. In our initial model, we discretized continuous attributes by splitting them into two groups at the point with the minimum expected information requirement, in accordance with the standard c4.5 algorithm. Using five-fold cross-validation, we calculated the average accuracy of our initial model for each data set. Our initial model yielded a 75.72% average accuracy across both data sets. The average execution time of our initial model was 1,541.57 s for the census Income data set and 50.54 s for the Heart Disease data set. We then optimized our model by applying MSD-Splitting, which discretizes continuous attributes by splitting them into four groups using the mean and the two values one standard deviation away from the mean as split points. The accuracy of our model improved by an average of 5.11% across both data sets, while the average execution time reduced by an average of 96.72% for the larger census Income data set and 46.38% for the Heart Disease data set.

关键词： c4.5 algorithm decision tree data mining machine learning classification

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Academic Analytics: Applying c4.5 Decision Tree algorithm in Predicting Success in the Licensure Examination of Graduates 4

Academic Analytics: Applying C4.5 Decision Tree Algorithm in...

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4th IEEE International conference on computer and communication Systems (IcccS)

作者： clarin, Jeffrey A. Romana, cherry Lyn c. Sta Feliscuzo, Larmie S. Cebu Inst Technol Univ Cebu Philippines

ISBN: (纸本)9781728113227

This study predicted success in The Licensure Examinations for Teachers (LET) applying c4.5 decision tree algorithm. This system was eyed to help the students and the academia improve their success rates in the LET. The following were the academic areas considered: Entrance examination raw scores;general weighted average (GWA) in the major field of specialization and in professional education and general education subjects;and, whether pass or fail in LET review results as well as LET Board results. There were a total of 348 instances studied, spread over a 5-year period, from 2012 to 2017. The size of the c4.5 pruned tree totaled to 16 with 10 leaves. The predictive capacity of the system was found to be almost perfect, evidenced by the generated Kappa value of 0.8195. Therefore, should there be any predicted failure, the teachers and deans could promptly provide intervention programs to improve the scores and GWA of students, which would eventually redound to the success in the LET. This study was conducted at Aklan State University (ASU), (Philippines), because at current times, there is still no academic analytics system installed for this purpose. consequently, ASU was considered as the test case and the datasets utilized in this study were from ASU. In essence, this study could help in accreditation matters, particularly in tracking down the LET success rates of the teacher graduates of HEIs, ASU included.

关键词： academic analytics c4.5 algorithm decision tree performance prediction KDD

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Research on campus Digital Teaching System of Smart u Based on Data Mining algorithm 3

Research on Campus Digital Teaching System of Smart u Based ...

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3rd IEEE International conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Wang, Guoyun Baise University Guangxi Baise533000 China

ISBN: (纸本)9798350380989

With the continuous development of data mining technology, the informatization level of colleges and universities is gradually improving. However, the traditional digital teaching system has many problems such as low utilization rate of data resources, non-sharing of information and data between systems, and it is difficult to meet the emerging new needs of colleges and universities. Therefore, it is of great significance to study the smart u campus digital teaching system based on data mining. Aiming at the problems of many links, serious information islands and low data utilization rate in traditional university teaching, this paper studies the smart u campus digital teaching system based on data mining. The core module of the system is the decision analysis module, which is based on the c4.5 algorithm framework, reduces the decision number of threshold points according to the Fayyad boundary point decision principle, and uses the equivalent infinitesimal principle to simplify the calculation of information entropy, overcoming the disadvantages of c4.5 algorithm. The module generates decision tree, generates classification rules through IF-THEN form, and realizes achievement prediction. The experimental data in this paper is the teaching data of a university. The experimental results show that the designed system can effectively realize data preprocessing, complete the learning effect prediction, provide personalized guidance for students, and realize the digital teaching work of smart u campus. © 2024 IEEE.

关键词： c4.5 algorithm Data mining Digital teaching Wisdom u campus

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comparative study of satellite multispectral image data processing with MapReduce and classification algorithm

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INTERNATIONAL JOURNAL OF cOMPUTER APPLIcATIONS IN TEcHNOLOGY 2022年第3期68卷 223-227页

作者： Rajyalakshmi, ch Rao, Katta Subba Rao, R. Rajeswara B V Raju Inst Technol Dept Comp Sci & Engn Hyderabad Telangana India Jawaharlal Nehru Technol Univ JNTUK Dept Comp Sci & Engn Kakinada Andhra Pradesh India Univ Coll Engn Vizianagaram UCEV Vizianagaram Andhra Pradesh India

Now that Big Data has amassed a significant amount of data, it is available in both structured and unstructured formats. Unstructured data processing is difficult to generate by individuals (e.g. Twitter data) or even sensors (e.g. satellites, videos) with data sizes ranging from gigabytes to terabytes and peta-bytes. We can easily analyse and classify different trends in unstructured data sets if the right analytical approach is used, while keeping data quality and size in mind. Early warning forecasts, which are based on satellite imagery and radar sensor data, are a major problem in the real world. To obtain a better understanding of Big data a proper architecture for the analysis of various classifications of satellite imagery patterns using Hadoop technology should be proposed. Different classification methods for different satellite imagery pattern classification methods are segregated in the proposed architecture to reduce time and improve efficiency and will get scalability results to increase the performance of classification patterns to large data sets.

关键词： c4.5 algorithm satellite image Hadoop Google's MapReduce big data

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classification algorithm for Predicting the Graduation Level: case Study of e-Learning in Telkom University 6th

Classification Algorithm for Predicting the Graduation Level...

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6th World conference on Smart Trends in Systems, Security and Sustainability, WS4 2022

作者： Hasibuan, M. Azani Andreswari, Rachmadita Lubis, Muharman Perdana, Fikli Fakhrurroja, Hanif Department of Information System Telkom University Bandung Indonesia

ISBN: (纸本)9789811976599

E-learning is one of the technologies used to support the learning process in university level. Telkom University with the university base on technology, Telkom University so intensively apply the learning using e-learning in order to become effective and efficient method. E-learning is used to reduce the cost and time spent learning in an offline class. With e-learning a review system can be done anywhere, because students do not need to come to the classroom to study. Simply by passing the Internet network can already attend the class. With the existing data in the e-learning system can be obtained some information by processing the existing data. This research will be predictions of student data contained in e-learning to determine the performance of algorithms c4.5, Naïve Bayes and Bayesian network, which will be used to determine what factors affect the level of graduation of students to a course. The process will go through several stages of data retrieval, preprocessing and classification, and the process will be done gradually to get the results of all the process. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Bayesian network c4.5 algorithm classification e-learning Naïve bayes

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