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Network Intrusion Detection by Multi-group Mathematical Programming based Classifier

Network Intrusion Detection by Multi-group Mathematical Prog...

引用

IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Gang Kou Yi Peng Yong Shi Zhengxin Chen College of Information Science and Technology University of Nebraska Omaha Omaha NE USA Research Center on Data Technology & Knowledge Economy Graduate School of Chinese Academy of Sciences China

The growing number of computer network attacks or intrusions has caused huge lost to companies, organizations, and governments during the last decade. Intrusion detection, which aims at identifying and predicting network attacks, is a fast developing area that has attracted attention from both industry and academia. Technologies have been developed to detect network intrusions using theories and methods from statistics, machine learning, soft computing, mathematics, and many other fields. We have previously proposed multiple criteria linear programming (MCLP) and multiple criteria nonlinear programming (MCNP) models for two-group intrusion detection. Although these models achieve good results in two-group classification problems, they perform poorly on multi-group situations. In order to solve the problem, we introduce the kernel concept into multiple criteria models in this paper. Experimental results show that the new model provides both high classification accuracies and low false alarm rates in three-group and four-group intrusion detection

关键词： Intrusion detection Mathematical programming Computer networks Linear programming Mathematical model Government Statistics Machine learning Mathematics data mining

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A Systemic Framework for the Field of data Mining and knowledge Discovery

A Systemic Framework for the Field of Data Mining and Knowle...

引用

IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Yi Peng Gang Kou Yong Shi Zhengxin Chen College of Information Science and Technology University of Nebraska Omaha Omaha NE USA Research Center on Data Technology & Knowledge Economy Graduate School of Chinese Academy of Sciences China

This paper proposes a systemic framework that attempts to define the domain and major areas of data mining and knowledge discovery (DMKD). Grounded theory approach, a qualitative method that inductively develops an understanding of phenomena, is adopted to build the framework. Using a large collection of DMKD literature, including DMKD journals, conference proceedings, syllabuses, and dissertations, this study develops a framework of eight main areas for the field: (1) foundations of DMKD; (2) learning methods & techniques; (3) mining complex data; (4) high-performance & distributed data mining; (5) data mining software & systems; (6) data mining process & project; (7) data mining applications; (8) data mining tasks. The last area is suggested as the central theme of the field

关键词： data mining Statistics Machine learning Delta modulation Educational institutions Information science Law Legal factors research and development Drives

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A Hybrid Strategy for Clustering data Mining Documents

A Hybrid Strategy for Clustering Data Mining Documents

引用

IEEE International Conference on data Mining Workshops (ICDM Workshops)

With the increase in the number of electronic documents, it is hard to manually organize, analyze and present these documents efficiently. Document clustering, which automatically groups similar or related documents together, has been used in practical applications to understand the contents and structures of documents. Although a variety of methods and algorithms have been proposed, it is still a challenging task to generate meaningful document clusters. This paper uses an approach that combines quantitative and qualitative methods in order to create high-quality clusters for a collection of data mining and knowledge discovery (DMKD) publications. The quantitative method extracts a list of noun/noun phrases from the DMKD documents and uses an optimization procedure from CLUTO toolkit to assign documents to clusters. The qualitative method uses grounded theory to identify major categories of the documents to improve the comprehensibility of resultant clusters. The results demonstrate that the strategy produces more meaningful clusters than single-term k-way clustering algorithm in terms of internal metrics and human assessment

关键词： data mining Clustering algorithms Partitioning algorithms Humans Classification algorithms Organizing Information retrieval databases Educational institutions Information science

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data Envelopment Analysis Assessment Machine

Data Envelopment Analysis Assessment Machine

引用

IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Quanling Wei Hong Yan College of Economics Chinese Academy of Sciences Research Center on Data Technology & Knowledge Economy Department of Logistics The Hong Kong Polytechnic University Hong Kong China

This word extends the DBA method to a large number DMU evaluation process, called "DEA assessment machine", for evaluating consecutive data. The authors identify the intersection form of the production possibility set for a given set of DMUs, which is called "training set". The process can then easily assess a newly coming DMU for its different properties, such as technical efficiency, returns to scales and evidence of congestion, by simply checking on a set of linear inequalities. It thus provides an efficient and effective method for dealing with large number of data and can be regarded as a complementary approach for data mining

关键词： data envelopment analysis Production data mining Decision making Educational institutions Strontium Logistics Tiles Couplings data analysis

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Recent trends in data Mining (DM): Document Clustering of DM Publications

Recent trends in Data Mining (DM): Document Clustering of DM...

引用

International Conference on Services Systems and Services Management, ICSSSM

作者： Yi Peng Gang Kou Zhengxin Chen Yong Shi Peter Kiewit Institute ofInformation Science Technology & Engineering University of Nebraska Omaha NE USA Chinese Academy of Sciences Research Center on Data Technology & Knowledge Economy Chinese Academy of Sciences Beijing China

data mining (DM) brings knowledge and theories from several fields including databases, machine learning, optimization, statistics, and data visualization and has been applied to various real-life applications. A large amount of data mining articles have been published. The goal of this study is to establish an overview of the past and current data mining research activities from the title and abstract more than 1400 textual documents collected from premier data mining journals and conference proceedings. Specifically, this study applied document clustering approaches to determine which subjects had been studied over the last several years, which subjects are currently popular, and describe the longitudinal changes of data mining publications

关键词： Delta modulation data mining Machine learning Conference proceedings IEL data engineering Visual databases Statistics data visualization Software libraries

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Bounded Support Vector Machines, Semidefinite

Bounded Support Vector Machines, Semidefinite

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Jianping Li Zhenyu Chen Weixuan Xu Research Center on Data Technology & Knowledge Economy Chinese Academy and Sciences Beijing China Graduate School of Chinese Academy of Sciences Beijing China Institute of Policy & Management Chinese Academy and Sciences Beijing China

Credit risk assessment is a basic and critical factor in credit risk management. In addition to conventional statistical method, neural network, decision tree and Support Vector Machine are the popular methods in this field in recent years. However, they all have weakness in two aspects: poor classification accuracy for unbalanced data and poor interpretability in real applications. A novel method, called Least Squares Support Feature Machine (LS-SFM), is proposed to reduce the misclassification cost and achieve an interpretable model by introducing the single feature kernel and sampling method. One important character of LS-SFM is that it can deliver the significance of each feature to users Our experiment on a real credit card dataset shows good performance. LS-SFM outperforms some well-known methods in several aspects.

关键词： Support vector machines Support vector machine classification Risk management Statistical analysis Neural networks Decision trees Least squares methods Costs Kernel Sampling methods

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Application of Clustering Methods to Health Insurance Fraud Detection

Application of Clustering Methods to Health Insurance Fraud ...

引用

International Conference on Services Systems and Services Management, ICSSSM

作者： Yi Peng Gang Kou Alan Sabatka Zhengxin Chen Deepak Khazanchi Yong Shi Peter Kiewit Institute of Information Science Technology & Engineering University of Nebraska Omaha Omaha NE USA Mutual of Omaha Omaha NE USA Chinese Academy of Sciences Research Center on Data Technology & Knowledge Economy Chinese Academy of Sciences Beijing China

Health insurance fraud detection is an important and challenging task. Traditionally, insurance companies use human inspections and heuristic rules to detect fraud. As the size of databases increases, the traditional approaches may miss a great portion of fraud for two main reasons. First, it is impossible to detect all health care fraud by manual inspection over large databases. Second, new types of health care fraud emerge constantly. SQL operations based on heuristic rules cannot identify those new emerging fraud schemes. Such a situation demands more sophisticated analytical methods and techniques that are capable of detecting fraud activities from large databases. The goal of this paper is to understand and detect suspicious health care frauds from large databases using clustering technique. Specifically, this paper applies two clustering methods, SAS EM and CLUTO, to a large real-life health insurance dataset and compares the performances of these two methods

关键词： Clustering methods Insurance Medical services Inspection Synthetic aperture sonar Humans data mining Spatial databases Cities and towns Input variables

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Least Squares Support Feature Machine

Least Squares Support Feature Machine

引用

International Conference on Computational Intelligence and Security

作者： Zhenyu Chen Jianping Li Chinese Academy of Sciences Beijing China Institute of Policy & Management Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Institute of Policy & Management Beijing 100080 China Chinese Academy of Sciences Research Center on Data Technology & Knowledge Economy Beijing 100080 China. ljp@***

ISBN: (纸本)1424406048

In many cases, data are represented as high dimensional feature vectors. It makes the feature selection necessary to reduce the computational burden, improve the generalization ability and the interpretability. In this paper, we present a novel feature selection method which is named least squares support feature machine (LS-SFM). In comparison with SVM and LS-SVM, this method has two outstanding properties. Firstly, the convex combinations of basic kernels are used as the kernel and each basic kernel makes use of a single feature. It makes the feature selection problem which can not be solved in the context of SVM transformed into an ordinary multiple parameters learning problem. Secondly, those parameters are learned by a two stage iterative algorithm. A 1-norm based regularized cost function is used to enforce sparsity of feature parameters. The 'support features' refer to the respective features with nonzero feature parameters. Some UCI datasets are used to demonstrate the effectiveness and efficiency of this approach

关键词： Least squares methods Support vector machines Kernel Support vector machine classification knowledge management technology management Cost function Bioinformatics Risk management Machine learning

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Leave-one-out bounds for support vector regression

Leave-one-out bounds for support vector regression

引用

International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, IAWTIC 2005

作者： Yingjie, Tian Naiyang, Deng College of Science China Agricultural University Research Center on Data Technology and Knowledge Economy Chinese Academy of Sciences

ISBN: (纸本)0769525040

The success of Support Vector Machine(SVM) depends critically on the kernel and the parameters in it. One of the most reasonable approaches is to select the kernel and the parameters by minimizing the bound of Leave-one-out(Loo) error. However, the computation of the Loo error is extremely time consuming. Therefore, an efficient strategy is to minimize an upper bound of the Loo error, instead of the error itself. In fact, for Support Vector Classification (SVC), some famous bounds have been proposed. This paper is concerned with Support Vector Regression (SVR). We derive two Loo bounds for two algorithms of SVR. In order to show the validity, preliminary experiments are also presented. © 2005 IEEE.

关键词： Learning systems

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A multi-class classification algorithm based on ordinal regression machine

A multi-class classification algorithm based on ordinal regr...

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作者： Zhixia, Yang Naiyang, Deng Yingjie, Tian College of Science China Agricultural University College of Mathematics and System Science Xinjiang University Research Center on Data Technology and Knowledge Economy Chinese Academy of Sciences

ISBN: (纸本)0769525040

Multi-class classification is an important and on-going research subject in machine learning. In this paper, we propose a multi-class classification algorithm based on ordinal regression algorithm using 3-class classification. This algorithm is similar to Algorithm K-SVCR and Algorithm v-K-SVCR, but it includes fewer parameters. Another advantage of our algorithm is that, for the K-class classification problem, our algorithm can be extended to using p-class classification with 2 &le p &le K. Numerical experiments on artificial data sets and benchmark data sets show that the algorithm is reasonable and effective. © 2005 IEEE.

关键词： Classification (of information)

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