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International conference on Emerging computation and Information technologies for Education (EcIcE 2012)

作者： Gu, Ping Zhou, Qi Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Guangxi Peoples R China

ISBN: (纸本)9783642284656

Data mining is a new technology and has successfully applied on a lot of fields. In this paper we applied a decision tree for mining the information that hides inside the student scores. In this paper, the c4.5 decision tree algorithm is improved. Using this improved algorithm, the decision tree model is produced and the classification rules are extracted. Finally, student performances are predicted. This technology helps a lot in middle school teaching improvement, and the quality of teaching becomes better.

关键词： Data mining c4.5 algorithm college exam scores prediction

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cT4RDD: classification trees for research on digital divide

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EXPERT SYSTEMS WITH APPLIcATIONS 2013年第14期40卷 5779-5786页

作者： coria, Sergio R. Mondragon-Becerra, Rosibelda Perez-Meza, Monica Ramirez-Vasquez, Sandra K. Martinez-Pelaez, Rafael Barragan-Lopez, Dario Avila-Barron, Oswaldo R. Univ Sierra Sur Inst Informat Miahuatlan Porfirio Diaz 70800 Oaxaca Mexico

This paper presents cT4RDD (classification trees for research on digital divide), a novel methodology for the quantitative analysis and modeling of the digital divide phenomenon with an approach of single country. It is inspired on the reputed Quinlan's c4.5 algorithm to automatically produce classification trees, as implemented in Witten & Frank's WEKA software toolkit. The methodology is created and evaluated on data from the 2010 Mexican Population and Housing census that include a number of variables whose interactions involve aspects of the phenomenon;particularly, interactions among Internet service presence in households and a number of features regarding educational and economical levels, genders, ages, housing characteristics, ratios of indigenous population, etc. Discretization is used to represent percentages of presence of Internet in households of municipalities as a nominal target attribute to produce classification trees. Results suggest that the methodology can produce quantitative profiles that describe similarities and differences among a series of municipality classes that present different percentages of presence of Internet in households. The discovered profiles provide scholars, government officials and enterprise managers with valuable insight for research, planning and decision making. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： Digital divide analysis Digital divide modeling Digital divide measurement census data mining c4.5 algorithm J4.8 algorithm

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Automated Evaluation Results Analysis With Data Mining algorithms

Automated Evaluation Results Analysis With Data Mining Algor...

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12th European conference e-Learning (EcEL)

作者： Bouarab-Dahmani, Farida Tahi, Razika Univ Tizi Ouzou FGEI Fac Dept Comp Sci Tizi Ouzou Algeria Univ Boumerdes Dept Econ Lab Elect Ind Enterprises Boumerdes Algeria

ISBN: (纸本)9781909507845

One of the challenges of research on Technology for Human Learning (THL) and the competency based pedagogy is the personalization of learning. This customization begins with the design of educational systems, applying the strategies defined to monitor and assist the learner in his learning by evaluating his knowledge, skills and detecting and analyzing errors. In this respect, formative evaluation is the process used to capture data on the strengths and weaknesses of a learner during a learning period. These data, to be useful, must be objectively analyzed so that they can be used to manage the following sessions. Data mining is a field at the intersection of statistics and information technology (databases, artificial intelligence, learning etc) whose goal is to discover "interesting" knowledge structures from a set of data. Following our research work on evaluation process and THL, applied to e-learning systems [2] [3] [4], we discuss in this paper a learning cycle with feedback loop integrating formative evaluation followed by the application of data mining algorithms after each learning session. We experimented with a set of tests, the exploration of learners' errors, obtained from a self e-learning by doing tool for the algorithmic domain. We particularly present, in this work, the results given by the application of the c4.5 algorithm for classification and the A Priori one for association rules deduction. These two algorithms were executed via the tool Weka after a data preparing process integrating a relational algebra calculation and format adjustment actions to get adequate input data for Weka algorithms. The results given by these experiments have proved interesting knowledge to complete the learner's profile and facilitate decisions about further learning / teaching / tutoring particularly in the case of distance education.

关键词： data mining e-learning formative assessment a priori algorithm c4.5 algorithm educational data mining

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Diagnosis of coronary Artery Disease Using cost-Sensitive algorithms

Diagnosis of Coronary Artery Disease Using Cost-Sensitive Al...

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12th IEEE International conference on Data Mining (IcDM)

作者： Alizadehsani, Roohallah Hosseini, Mohammad Javad Sani, Zahra Alizadeh Ghandeharioun, Asma Boghrati, Reihane Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9780769549255;9781467351645

One of the main causes of death the world over are cardiovascular diseases, of which coronary artery disease (cAD) is a major type. This disease occurs when the diameter narrowing of one of the left anterior descending, left circumflex, or right coronary arteries is equal to or greater than 50 percent. Angiography is the principal diagnostic modality for the stenosis of heart vessels;however, because of its complications and costs, researchers are looking for alternative methods such as data mining. This study conducts data mining algorithms on the Z-Alizadeh Sani dataset which has been collected from 303 random visitors to Tehran's Shaheed Rajaei cardiovascular, Medical and Research center. In this paper, the reason of effectiveness of a preprocessing algorithm on the dataset is investigated. This algorithm which has been merely introduced in our previous works, extracts three new features from the dataset. These features are then used to enrich the primary dataset in order to achieve more accurate results. Moreover, despite the fact that misclassification of diseased patients has more side effects than that of healthy ones, to the best of our knowledge cost-sensitive algorithms have yet to be used in this field. Therefore, in this paper 10-fold cross validation on cost-sensitive algorithms along with base classifiers of Naive Bayes, Sequential Minimal Optimization (SMO), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and c4.5 were employed. As a result, the SMO algorithm has yield to very high sensitivity (97.22%) and accuracy (92.09%) rates, the likes of which have not been reported simultaneously in the existing literature.

关键词： component Naive Bayes algorithm Data Mining coronary Artery Disease c4.5 algorithm cost Sensitive algorithms Feature Extraction

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The Application of Improved Decision Tree algorithm in the Electric Power Marketing

The Application of Improved Decision Tree Algorithm in the E...

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World Automation congress (WAc)

作者： Meng, Jianliang Yang, Yanyan N China Elect Power Univ Dept Comp Engn Baoding Peoples R China

ISBN: (纸本)9781467344975

Data mining technology has been widely studied and applied since its inception, the researches on c4.5 algorithm in the aspects of data classification technology have comparative matured, but the application of electric power marketing data mining aspects is relatively less. Decision tree classification technology was discussed in this paper, and detailed introduced an improved c4.5 decision tree classification algorithm, and then used the actual electric power marketing data as the basis for data mining. Through analysis of the results of data mining, which show that the decision tree algorithm is efficient and accurate, is the preferred tool of electric power data mining. The decision tree algorithm is used in electric power enterprises, mines out marketing knowledge of behind the hidden electric power data, so as to provide help for electric power enterprises operation and decision-making.

关键词： decision tree c4.5 algorithm electric power marketing data mining

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Improving network security using machine learning techniques

Improving network security using machine learning techniques

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IEEE International conference on computational Intelligence and computing Research

作者： Shaik Akbar J. A. chandulal K. Nageswara Rao G. Sudheer Kumar Department of Computer Science and Engineering SVIET Nandamuru Andhra Pradesh India Department of Computer Science and Engineering GITAM University Visakhapatnam Andhra Pradesh India Department of Computer Science and Engineering P.V.P. S. I .Technology Vijayawada Andhra Pradesh India

ISBN: (纸本)9781467313421

Discovery of malicious correlations in computer networks has been an emergent problem motivating extensive research in computer science to develop improved intrusion detecting systems (IDS). In this manuscript, we present a machine learning approach known as Decision Tree (c4.5) algorithm and Genetic algorithm, to classify such risky/attack type of connections. The algorithm obtains into consideration dissimilar features in network connections and to create a classification rule set. Every rule in rule set recognizes a particular attack type. For this research, we implement a GA, c.45 and educated it on the KDD cup 99 data set to create a rule set that can be functional to the IDS to recognize and categorize dissimilar varieties of assault links. During our study, we have developed a rule set contain of six rules to classify six dissimilar attack type of connections that fall into 4 modules namely DoS, U2R, root to local and probing attacks. The rule produces works with 93.70% correctness for detecting the denial of service type of attack connections, and with significant accuracy for detecting the root to local, user to root and probe connections. Results from our experiment have given hopeful results towards applying enhanced genetic algorithm for NIDS.

关键词： Intrusion Detection Genetic algorithm c4.5 algorithm KDDcup'p99 computer networks Data mining

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Hierarchical fuzzy clustering decision tree for classifying recipes of ion implanter

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EXPERT SYSTEMS WITH APPLIcATIONS 2011年第1期38卷 933-940页

作者： Horng, Shih-cheng Yang, Feng-Yi Lin, Shieh-Shing Chaoyang Univ Technol Dept Comp Sci & Informat Engn Taichung 41349 Taiwan Natl Yang Ming Univ Dept Biomed Imaging & Radiol Sci Taipei Taiwan St Johns Univ Dept Elect Engn Taipei Taiwan

In this paper, we propose a hierarchical fuzzy clustering decision tree (HFcDT) for the classification problem with large number of classes and continuous attributes. The HFcDT combines a division-degree matrix based hierarchal clustering technique with the entropy-based c4.5 decision tree algorithm. A hierarchical clustering concept is introduced to achieve a finer fuzzy partition. The hierarchical clustering technique splits the data set into leaf clusters using splitting attributes based on a division-degree matrix and fuzzy rules. The leaf clusters consisting of the data of more than one class will be further classified using the c4.5 algorithm. We have successfully applied the HFcDT for classifying recipes of the working wafers in an ion implanter, and compared the classification results and the training time with the existing software See5 and cART. The comparison results show that the HFcDT not only performs better than See5 and cART in the aspect of 10-fold cross validation for the average of total classification error rates but also consumes less training time. Thus, HFcDT obtains a very successful classification result. This also demonstrates why the hierarchical clustering technique helps reduce the computational complexity of the c4.5 algorithm. (c) 2010 Elsevier Ltd. All rights reserved.

关键词： classification Fuzzy clustering Decision tree c4.5 algorithm Ion implanter

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Improving naive Bayes classifier by dividing its decision regions

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Journal of Zhejiang University-Science c(computers and Electronics) 2011年第8期12卷 647-657页

作者： Zhi-yong YAN Gong-fu XU Yun-he PAN Institute of Artificial Intelligence Zhejiang University Hangzhou 310027 China

classification can be regarded as dividing the data space into decision regions separated by decision *** this paper we analyze decision tree algorithms and the NBTree algorithm from this ***,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 ***,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 *** propose a second division (SD) algorithm and three soft second division (SD-soft) algorithms to train classifiers in decision regions of the naive Bayes *** four novel algorithms all generate two-level classifier trees with the naive Bayes classifier as root *** 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 ***,we conduct experiments on 30 data sets from the Uc Irvine (UcI) *** 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 *** experiments indicate that our three SD-soft algorithms can achieve better generalization abilities than the SD algorithm when argument values are selected appropriately.

关键词： Naive Bayes classifier Decision region NBTree c4.5 algorithm Support vector machine (SVM)

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The Application of Improved Decision Tree algorithm in the Electric Power Marketing

The Application of Improved Decision Tree Algorithm in the E...

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World Automation congress

作者： Jianliang Meng Yanyan Yang Department of Computer Engineering North China Electric Power University Baoding China

ISBN: (纸本)9781467344975

关键词： decision tree c4.5 algorithm electric power marketing data mining

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Identification of Encryption algorithm Using Decision Tree

Identification of Encryption Algorithm Using Decision Tree

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1st International conference on computer Science and Information Technology

作者： Manjula, R. Anitha, R. PSG Coll Technol Dept Math & Comp Applicat Coimbatore Tamil Nadu India

ISBN: (纸本)9783642178801

The task of identification of encryption algorithm from cipher text alone is considered to be a challenging one. Very few works have been done in this area by considering block ciphers or symmetric key ciphers. In this paper, we propose an approach for identification of encryption algorithm for various ciphers using the decision tree generated by c4.5 algorithm. A system which extracts eight features from a cipher text and classifies the encryption algorithm using the c4.5 classifier is developed. Success rate of this proposed method is in the range of 70 to 75 percentages.

关键词： ciphers Decision tree c4.5 algorithm

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