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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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pattern based network security using decision trees and support vector machine

Pattern based network security using decision trees and supp...

引用

international conference on Electronic Computer Technology

作者： Pachghare, V.K. Kulkarni, Parag Dept. of Computer Engineering and Information Technology College of Engineering Pune India College of Engineering Pune Pune India

ISBN: (纸本)9781424486779

Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. WEKA is software which is capable of doing work on various decision tree algorithms and support vector machine. In this paper, the comparative study of all Decision Tree algorithms is done. The training time, Accuracy and size of tree are the parameters used as performance measures. It is concluded that J48 Graft algorithm performs better than other algorithms. The dataset used is KDD cup'99 dataset. This dataset contains normal as well as abnormal packets. The dataset is highly uneven. We worked out on some 1000 selected packets. The support vector machine algorithms have the ability to be trained and 'learn' in a given environment. This feature can be used in connection with an intrusion detection system, where the support vector machine algorithm can be trained to detect intrusions by recognizing patterns of an intrusion. This paper outlines an investigation on the support vector machine models and choice of one of them for evaluation and implementation. The work also includes works on computer networks, providing a description and analysis of the structure of the computer network in order to generate network features. © 2011 IEEE.

关键词： Support vector machines

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Prediction and assessment of student learning outcomes in calculus A decision support of integrating data mining and Bayesian belief networks

Prediction and assessment of student learning outcomes in ca...

引用

2011 3rd international conference on Computer Research and Development(ICCrd 2011)

作者： Kevin Fong-Rey Liu Jia-Shen Chen Department of Safety Health and Environmental Engineering Ming Chi University of Technology Taipei 24301Taiwan"ROC" Department of Safety Health and Environmental Engineering Ming Chi University of Technology Taipei 24301 Taiwan "ROC"

A decision support system based on data mining (DM) and Bayesian belief networks (BBN) is proposed to predict the student learning outcomes and takes the calculus course as an example to help students overcome their learning difficulties. Total of 427 freshmen in Ming Chi University of Technology (Taiwan) did questionnaires to assist this study. The methodologies involves four steps: fuzzy theory to identify the factors on learning outcomes;data mining to construct influence diagram;machine learning to establish the probability tables in BBN;and the model to predict the exam scores at the beginning of course and thereby to help students enhance their scores according to their weakness.

关键词： learning outcome data mining Bayesian belief networks

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Sentiment classification of online product reviews using product features

Sentiment classification of online product reviews using pro...

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international conference on data mining and Intelligent Information Technology Applications (ICMiA)

作者： Neda Aleebrahim Mohammad Fathian Mohammad Reza Gholamian Department of Electronic Commerce Iran University of Science and Technology Iran

There is a great number of online product reviews on the Internet which needs to be organized. In this paper, we consider the problem of sentiment classification of online reviews to determine the overall semantic orientation of customer reviews. Our proposed method for review classification is a supervised machine learning method based on extracting product features and the polarity of opinions expressed about the features.

关键词： Feature extraction Semantics Support vector machine classification Itemsets data mining Vectors

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Design of automatically adaptable web wrappers

Design of automatically adaptable web wrappers

引用

作者： Ferrara, Emilio Baumgartner, Robert Department of Mathematics University of Messina Messina Italy Lixto Software GmbH Vienna Austria

ISBN: (纸本)9789898425409

Nowadays, the huge amount of information distributed through the Web motivates studying techniques to be adopted in order to extract relevant data in an efficient and reliable way. Both academia and enterprises developed several approaches of Web data extraction, for example using techniques of artificial intelligence or machine learning. Some commonly adopted procedures, namely wrappers, ensure a high degree of precision of information extracted from Web pages, and, at the same time, have to prove robustness in order not to compromise quality and reliability of data themselves. In this paper we focus on some experimental aspects related to the robustness of the data extraction process and the possibility of automatically adapting wrappers. We discuss the implementation of algorithms for finding similarities between two different version of a Web page, in order to handle modifications, avoiding the failure of data extraction tasks and ensuring reliability of information extracted. Our purpose is to evaluate performances, advantages and draw-backs of our novel system of automatic wrapper adaptation.

关键词： data mining

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Extracting Semantic Knowledge from Twitter

Extracting Semantic Knowledge from Twitter

引用

3rd IFIP WG 8.5 international conference on Electronic Participation (ePart)

作者： Teufl, Peter Kraxberger, Stefan Graz Univ Technol IAIK A-8010 Graz Austria

ISBN: (纸本)9783642233333

Twitter is the second largest social network after Facebook and currently 140 millions Tweets are posted on average each day. Tweets are messages with a maximum number of 140 characters and cover all imaginable stories ranging from simple activity updates over news coverage to opinions on arbitrary topics. In this work we argue that Twitter is a valuable data source for e-Participation related projects and describe other domains were Twitter has already been used. We then focus on our own semantic-analysis framework based on our previously introduced Semantic patterns concept. In order to highlight the benefits of semantic knowledge extraction for Twitter related e-Participation projects, we apply the presented technique to Tweets covering the protests in Egypt starting at January 25th and resulting in the ousting of Hosni Mubarak on February 11th 2011. Based on these results and the lessons learned from previous knowledge extraction tasks, we identify key requirements for extracting semantic knowledge from Twitter.

关键词： Semantic patterns Twitter mining e-Participation Semantic Analysis Trend Analysis Semantic Search machine learning Social Network Analysis

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A hybrid multiclass classifier based on artificial immune algorithm and support vector machine

A hybrid multiclass classifier based on artificial immune al...

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international conference on data mining and Intelligent Information Technology Applications (ICMiA)

作者： Hui-Ping Cheng Chuen-Sheng Cheng Department of Business Innovation and Development MingDao University Changhua Taiwan Department of Industrial Engineering and Management Yuan-Ze University Taoyuan Taiwan

Developing an effective medical diagnosis system for many diseases, such as thyroid gland disease, to assist physicians in hospitals has become a high priority for many researchers and clinical centers. In fact, existing medical diagnostic techniques often have to diagnose the risk of misdiagnosis. The purpose of this paper is to develop an efficient classifier to improve medical diagnosis performance of thyroid gland disease. In this work, the medical dataset of thyroid gland disease that represent multiclass classification problem was selected from the University of California Irvine machine learning Repository. The proposed approach combined support vector machines with an artificial immune system as the diagnostic classifier, which is called the AIS-based machine learning classifier. The diagnosis results were identified, and the accuracies of the classification rate were evaluated. The classification results demonstrated that the proposed approach can give considerable improvements over those reported in previous studies.

关键词： Immune system Diseases Support vector machines machine learning Glands Cloning Kernel

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An E-SMOTE technique for feature selection in High-Dimensional Imbalanced dataset

An E-SMOTE technique for feature selection in High-Dimension...

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international conference on Electronic Computer Technology

作者： T. Deepa M. Punithavalli Computer Science Department Karpagam University Coimbatore Tamil Nadu India SNS College Coimbatore Tamil Nadu India

Feature Selection in High-Dimensional Imbalanced dataset (where one class outnumbers the other class) plays an imperative task in field of data mining and Bio-informatics. This paper proposes a new technique called E-SMOTE Technique for balancing the dataset and SVM classification for selecting the features. It is evaluated using micro array dataset.

关键词： Feature extraction Genetic algorithms Support vector machines data mining machine learning Bioinformatics Cancer

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Estimating diffusion probability changes for AsIC-SIS model from information diffusion results

Estimating diffusion probability changes for AsIC-SIS model ...

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3rd Asian conference on machine learning, ACML 2011

作者： Koide, Akihiro Saito, Kazumi Ohara, Kouzou Kimura, Masahiro Motoda, Hiroshi Graduate School of Management and Information of Innovation University of Shizuoka Japan School of Management and Information University of Shizuoka Japan Department of Integrated Information Technology Aoyama Gakuin University Japan Department of Electronics and Informatics Ryukoku University Japan Institute of Scientific and Industrial Research Osaka University Japan

We address the problem of estimating changes in diffusion probability over a social network from the observed information diffusion results, which is possibly caused by an unknown external situation change. For this problem, we focused on the asynchronous independent cascade (AsIC) model in the SIS (Susceptible/Infected/Susceptible) setting in order to meet more realistic situations such as communication in a blogosphere. This model is referred to as the AsIC-SIS model. We assume that the diffusion parameter changes are approximated by a series of step functions, and their changes are reflected in the observed diffusion results. Thus, the problem is reduced to detecting how many step functions are needed, where in time each one starts and how long it lasts, and what the hight of each one is. The method employs the derivative of the likelihood function of the observed data that are assumed to be generated from the AsIC-SIS model, adopts a divide-and-conquer type greedy recursive partitioning, and utilizes an MDL model selection measure to determine the adequate number of step functions. The results obtained using real world network structures confirmed that the method works well as intended. The MDL criterion is useful to avoid overfitting, and the found pattern is not necessarily the same in terms of the number of step functions as the one assumed to be true, but the error is always reduced to a small value. © 2011 A. Koide, K. Saito, K. Ohara, M. Kimura & H. Motoda.

关键词： pattern recognition

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A comparative study of pseudo-inverse computing for the extreme learning machine classifier

A comparative study of pseudo-inverse computing for the extr...

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international conference on data mining and Intelligent Information Technology Applications (ICMiA)

作者： Punyaphol Horata Sirapat Chiewchanwattana Khamron Sunat Department of Computer Science Faculty of Science Khon Kaen University Khon Kaen Thailand

Most feed-forward artificial neural network training algorithms for classification problems are based on an iterative steepest descent technique. Their well-known drawback is slow convergence. A fast solution is an Extreme learning machine (ELM) computing the Moore-Penrose inverse using SVD. However, the most significant training time is pseudo-inverse computing. Thus, this paper proposes two fast solutions to pseudo-inverse computing based on QR with pivoting and Fast General Inverse algorithms. They are QR-ELM and GENINV-ELM, respectively. The benchmarks are conducted on 5 standard classification problems, i.e., diabetes, satellite images, image segmentation, forest cover type and sensit vehicle (combined) problems. The experimental results clearly showed that both QR-ELM and GENINV-ELM can speed up the training time of ELM and the quality of their solutions can be compared to that of the original ELM. They also show that QR-ELM is more robust than GENINV-ELM.

关键词： Training Testing Matrix decomposition Equations Accuracy Diabetes Classification algorithms

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A new sampling technique and SVM classification for feature selection in high-dimensional Imbalanced dataset

A new sampling technique and SVM classification for feature ...

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international conference on Electronic Computer Technology

作者： T. Deepa M. Punithavalli Computer Science Department Karpagam University Coimbatore Tamil Nadu India Computer Science Department SNS College of Arts and Science Coimbatore Tamil Nadu India

Feature selection in high-dimensional Imbalanced dataset (where one class highly outnumbers the other class) is an exigent task in data mining. Feature selection refers to selecting a subset of features from the original dataset. This paper focus on two problems i) Balancing the dataset ii) extracting the features. A new technique called Evolutionary sampling technique [EST] is developed to balance the dataset and Support Vector machine [SVM] classification is used to calculate the accuracy and also to overcome the over fitting problem while sampling the dataset. The techniques are evaluated on a micro array dataset.

关键词： Feature extraction Support vector machines Genetic algorithms data mining Earth Observing System machine learning Cancer

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