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Random rough subspace based neural network ensemble for insurance fraud detection

Random rough subspace based neural network ensemble for insu...

引用

International Workshop on Computational Transportation Science

作者： Xu, Wei Wang, Shengnan Zhang, Dailing Yang, Bo Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769543352

In this paper, a random rough subspace based neural network ensemble method is proposed for insurance fraud detection. In this method, rough set reduction is firstly employed to generate a set of reductions which can keep the consistency of data information. Secondly, the reductions are randomly selected to construct a subset of reductions. Thirdly, each of the selected reductions is used to train a neural network classifier based on the insurance data. Finally, the trained neural network classifiers are combined using ensemble strategies. For validation, a real automobile insurance case is used to test the effectiveness and efficiency of our proposed method with two popular evaluation criteria including the percentage correctly classified (PCC) and the receive operating characteristic (ROC) curve. The experimental results show that our proposed model outperforms single classifier and other models used in comparison. The findings of this study reseal that the random rough subspace based neural network ensemble method can provide a faster and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. © 2011 IEEE.

关键词： Insurance

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A RFID mixed coding scheme based on huffman algorithm

A RFID mixed coding scheme based on huffman algorithm

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2011 International Conference on Management of e-Commerce and e-Government, ICMeCG 2011

作者： Jian, Jiang Guoqiong, Liao Hao, Wang Qizhi, Wan School of Information Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Nanchang Nanchang China

ISBN: (纸本)9780769545448

In RFID application systems with multiple packaging layers, labeling packaging relationship of objects in different packaging layers by encoding methods is a important technology field. Prefix-based labeling scheme is a method by which the packaging relationship of objects amounts to testing whether one object is an prefix of the other, whereas the region numbering scheme is a method by which an ancestor query amount to an interval containment test on the labels. This paper first presents a mixed-encoding scheme based on Huffman algorithm, then propose a variant of this encoding scheme in which new "virtual nodes" are involved in order to improve the stability of encoding. Our experiments prove that the mixed-based encoding scheme is effective due to the enhancement of update performance and query efficiency. © 2011 IEEE.

关键词： Encoding (symbols)

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Visual group identification method of technical competitors using LinLog graph clustering algorithm

Visual group identification method of technical competitors ...

引用

IEEE International Conference on Computer Science and Automation engineering

作者： Han, Hong-Qi An, Xiaomi Zhu, Donghua Wang, Xuefeng Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Management and Economics Beijing Institute of Technology Beijing 100872 China

ISBN: (纸本)9781424487257

Visualization technique is a powerful method used by science and technology intelligence analysis experts to identify technical competitor groups. Common visualization methods tend to create graphs meeting the aesthetic criteria instead of finding better clusters, and their analysis results may provide misleading information. A process model of technical group identification method was presented using LinLog graph clustering algorithm to find better competitor groups. In the model, technical similarity value of each pair of competitors is measured based on their R&D output in sub-fields, and two competitors have a link when they have high similarity value;LinLog algorithm, which is aimed at producing better clusters, was employed to layout graph with competitors as nodes, their links as edges and technology similarity values as weights of edges. Experiment results show the efficiency of presented method. © 2011 IEEE.

关键词： Visualization

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Spatial data mining under Smart Earth

Spatial data mining under Smart Earth

引用

2011 IEEE International Conference on Granular Computing, GrC 2011

作者： Wang, Shuliang International School of Software of Wuhan University Wuhan University Wuhan Hubei 430072 China Key Laboratory of Data Mining and Knowledge Engineering Hohhot China

ISBN: (纸本)9781457703713

Complex spatial data is the fundamental content of Smart Earth. Spatial data mining plays an important role in Smart Earth. In this paper, it is proposed that "Smart earth equals to Digital Earth plus the Internet of Things", which brings a new challenge for spatial data mining. First, the background of spatial data is summarized. Second, Smart Earth is put forward to be an integration of Digital Earth and the Internet of Things. Third, by analyzing the spatial data sources, types and structures, it uncovers that spatial data is the resource of Smart Earth. Fourth, spatial knowledge to discover in Smart Earth are analyzed on its connotations, extensions and categories. It indicates that spatial knowledge is the intelligent sources of Smart Earth. Fifth, based on spatial data characteristics and possible methodologies, the framework of spatial data mining is given under Smart Earth. Finally, the perspective of applicable fields is prospected for spatial data mining under Smart Earth. It is concluded that spatial data mining is one of the fundamental tools to achieve Smart Earth. © 2011 IEEE.

关键词： Internet of things

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Formal analysis of trusted computing: One case study

Formal analysis of trusted computing: One case study

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International Conference on Communications and Mobile Computing

作者： Zhou, HongWei Yuan, JinHui Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China School of Information Renmin University of China Beijing China Institute of Electronic Technology Zhengzhou Information Engineering University of China Zhengzhou China

ISBN: (纸本)9780769543574

LS2 is the logic to reason about the property of trusted computing. However, it lacks the capability of modeling the isolation provided by virtualization which is often involved in previous trusted computing system. With the support of changed LS2, we model three types of isolation. Moreover, we formally analyze the integrity measurement property of Trust Visor proposed recently which provides the isolated execution environment for security-sensitive code. © 2011 IEEE.

关键词： Virtualization

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B-IRP: An improving integrity report protocol for remote attestation

B-IRP: An improving integrity report protocol for remote att...

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International Conference on Communications and Mobile Computing

ISBN: (纸本)9780769543574

Trusted platform module (TPM) has little computation capability, and it is the performance bottleneck of remote attestation. In the scenario where the server is the attestation-busy entity which answers attestation requirement frequently, the massive delay is inevitable. Without the modification to TPM, we propose Batch Integrity Report Protocol (BIRP) to overcome the performance bottleneck. B-IRP bundles these requirements in interval as a batch requirement, and creates the response messages for these requirements with one expensive TPM operation. Discussion shows that B-IRP has improved the performance without loss of security. © 2011 IEEE.

关键词： Trusted computing

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Sentiment classification via L2-norm deep belief network 11

Sentiment classification via L2-norm deep belief network

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20th ACM Conference on Information and knowledge Management, CIKM'11

作者： Liu, Tao Li, Minghui Zhou, Shusen Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering MOE 100872 Beijing China School of Information Renmin University of China 100872 Beijing China Microsoft Asian Research and Development Group 100080 Beijing China Shenzhen Graduate School Harbin Institute of Technology 518055 Shenzhen China

ISBN: (纸本)9781450307178

Automatic analysis of sentiments expressed in large scale online reviews is very important for intelligent business applications. Sentiment classification is the most popular task of sentiment analysis, which is more challenging than traditional topic-based text classification. Basic features, such as vocabulary words, are not enough to classify sentiments well. Deep Belief Network (DBN) is introduced to discover more abstract features of sentiments. To capture full information of the features, large-size network can be constructed, but at the same time, large-size network tends to over fit the training data and even noise, which will reduce the generalization ability of the network. In this paper, L2-norm Deep Belief Network (L2DBN) is proposed, which uses L2-norm regularization to optimize the network parameters of DBN. L2DBN is first initialized by an unsupervised layer-wise training algorithm, and then fine-tuned by a supervised procedure. Network parameters are optimized using both classification loss and network complexity. Experimental results show that the proposed L2DBN outperforms the state-of-the-art method and the basic DBN on golden, noisy and heterogeneous datasets. © 2011 ACM.

关键词： Sentiment analysis

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Clustering XML search results based on content and structure similarity

Clustering XML search results based on content and structure...

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2011 International Conference on Management of e-Commerce and e-Government, ICMeCG 2011

作者： Min-Juan, Zhong Chang-Xuan, Wan De-Xi, Liu Xian-Pei, Jiao School of Information and Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9780769545448

Clustering XML search results is an effective way to improve performance. However, the key problem is how to measure similarity between XML documents. In this paper, we propose a semantic similarity measure method combining content with structure, in which a variety of XML document features, including term element frequency, term inverse element frequency, semantic weight of tag label and level information of the term, are analyzed and applied for computing the similarity between XML documents. In addition, two new performance evaluation methodology, namely Cluster Ratio Relevant and Docu Ratio Relevant, for clustering quality are introduced motivated by the observations of relevant documents distribution and the fact that collection has no classification information. Experiment results show that proposed similarity method(CAS measure)outperforms traditional document clustering(CO measure) in Cluster Ratio Relevant and Docu Ratio Relevant and produces better clustering quality. © 2011 IEEE.

关键词： XML

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Lawyer information integration and recommendation by multi-source information validation

Lawyer information integration and recommendation by multi-s...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Tao Liu Biao Fan He Hu Xiao-Yong Du Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China China School of Information Renmin University of China Beijing China

On the internet, all-round lawyer information is located at separated information sources, which prevent web users from effective information acquisition. In order to build a unified view of separated, heterogeneous, and often redundant lawyer information, we propose a new information integration method using multi-source information cross-validation. Based on the unified integrated data, a lawyer recommendation system is built. Several key technologies are presented and evaluated, including the multi-source information acquisition and validation. Experimental results indicate the key techniques used in the system are effective for lawyer information integration and recommendation.

关键词： Organizations Crawlers Search engines data mining Web pages databases Reliability

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A query-oriented summarization system for XML elements

A query-oriented summarization system for XML elements

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2011 International Conference on Management of e-Commerce and e-Government, ICMeCG 2011

作者： Liu, Dexi Wu, Shihan Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang 330013 China Songjiang Branch Shanghai Rural Commercial Bank Shanghai 201600 China

ISBN: (纸本)9780769545448

In document-center XML dataset, an element may contain so many text that users have to spend enough time to judge the elements returned by XML search engine are valuable or not. Query-orient XML summarization system aim to provide users a brief and readable substitution of the original retrieved elements according to the user's query, which can relieve user's reading burden effectively. In this work, we extract sentences from the results of XML search engine, and combine them as a summary. Experiments on the IEEE-CS datasets used in INEX show that, the query-oriented XML summary generated by our method is reasonable. © 2011 IEEE.

关键词： XML

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