2006 IEEE International Conference on Granular Computing最新文献

{"title":"Statistical analysis of high dimensional gene data","authors":"Yichuan Zhao, Yue Zhou","doi":"10.1109/GRC.2006.1635850","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635850","url":null,"abstract":"We consider the problem of constructing an additive risk model based on the right censored survival data with high dimensional covariates to predict the survival times of the cancer patients. We apply Partial Least Squares to reduce the dimension of the covariates and get the latent variables, say, components; these components can be used as new regressors to fit the extensional additive risk model. Also the time dependent AUC curve (area under the receiver operating characteristic (ROC) curve) is employed to assess how well the model predicts the survival time. This approach is illustrated by analysis of breast cancer dataset.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131287349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison of two partial matching strategies for classification of unseen cases","authors":"J. Grzymala-Busse, G. Sudre","doi":"10.1109/GRC.2006.1635921","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635921","url":null,"abstract":"This paper compares two partial matching strate- gies, selective and mixed, for classification of unseen cases. The selective partial matching is a novel approach for classification, while mixed partial matching was implemented in the LERS classification system several years ago. Though results of our experiments show that neither strategy is better than the other, an important conclusion is that it is crucial to implement both strategies since the correct choice of one of these strategies, for a specific data set, results in substantial improvement of the final classification.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130054920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expressing authorization in semantic web services","authors":"R. S. Patterson, J. Miller","doi":"10.1109/GRC.2006.1635919","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635919","url":null,"abstract":"Currently many Web services use authentication to make authorization decisions on an operation by operation basis. Semantic Web services need the ability to describe authorization for the purpose of Web service discovery. In this paper we propose a framework for describing authorization using Semantic annotations in WS-Policy. These annotations are used in the post-'Semantic Discovery Phase' to determine if a requester has permissions to invoke a published service.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116154068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantic reasoning study for rough logic about n-ary formulas","authors":"Lin Yan, Sui-Hua Wang, Xue-Dong Zhang","doi":"10.1109/GRC.2006.1635820","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635820","url":null,"abstract":"We begin this paper with a discussion of constructing a kind of formulas which are called n-ary formulas in an approximate space of rough set theory. These formulas are an expansion of the formulas in Pawlak rough logic, so that the domains of the n-ary formulas are extended from subsets of U to subsets of U n (=U×U×…×U) . In subsequent discussions we define five rough logical values based on Pawlak rough logic for the n-ary formulas in n-dimensional space, and study rough logical reasoning in semantics through these rough logical value operations. Of course, we get some theorems which indicate that some forms of logical reasoning in classical logic are also true in rough logic for some rough logical values, but because 5-value rough logic is different from classical 2-value logic, we naturally obtain some new properties.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126911890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methodology for fraud detection using rough sets","authors":"J. E. Cabral, João O. P. Pinto, K. S. C. Linares, Alexandra M. A. C. Pinto","doi":"10.1109/GRC.2006.1635791","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635791","url":null,"abstract":"This work proposes a methodology based on Rough Sets and KDD for fraud detection made by electrical energy consumers. This methodology does a detailed evaluation of the boundary region between normal and fraudulent costumers, identifying patterns of fraudulent behavior at historical data sets of electricity companies. Using these patterns, classification rules are derived, and they will permit the detection on the database of electricity companies of those clients that present fraudulent feature. When doing inspections with the proposed methodology, the rate of correctness and the quantity of detected frauds are increased, decreasing the losses with electricity fraud on Brazilian electrical energy distribution companies. One of the problems that Brazilian electrical energy distri- bution companies undergo are the commercial losses resulted from consumers electrical frauds. To decrease these losses, the companies realize in loco inspections to detect such frauds. The inspections are made by technicians that go to the con- sumer unit to evaluate equipments and electricity connections. Usually, company experts indicates which consumer unit must undergo the inspection. This decision is based on factors such as: unit with low consumption rate, high fraud incidence, and others. Since there is a very high number of consumer units, it is almost impossible for the expert to evaluate the behavior of each consumer unit and indicate which ones are suspect of fraud. Also, it is not viable to inspect all the consumer units, seeing that the number of fraudulent consumers is small compared to the total number of consumers. The rate of correct fraud identification of the electrical energy distribution companies goes between 5 to 10%. However, it is known that electrical energy distribution companies keep consumer information on theirs databases. This information can be used for the identification of behavior patterns. When finding a pattern that indicates a fraudulent behavior, the expert can recommend that consumers with this pattern must undergo inspection. The discovery process of these behavior patterns when using databases is called KDD (Knowledge Discovery in Databases) (1). This process","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125977316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Training radial basis function networks with differential evolution","authors":"Ting Yu, Xingshi He","doi":"10.1109/GRC.2006.1635817","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635817","url":null,"abstract":"In this paper, Differential Evolution (DE) algorithm, a new promising evolutionary algorithm, is proposed to train Radial Basis Function (RBF) network related to automatic configuration of network architecture. Classification tasks on data sets: Iris, Wine, New-thyroid and Glass are conducted to measure the performance of neural networks. Compared with a standard RBF training algorithm in Matlab neural network toolbox, DE achieves more rational architecture for RBF networks. The resulting networks hence obtain strong generalization abilities.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129164153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An agent-based dual-tier algorithm for clustering data streams","authors":"Dongbin Zhou, Lifeng Jia, Zhe Wang, Xiujuan Xu, Chunguang Zhou","doi":"10.1109/GRC.2006.1635855","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635855","url":null,"abstract":"Characteristics of data stream make it difficult for the clustering algorithms to satisfy the requirements on efficiency and effectiveness. This paper proposes a data stream clustering algorithm on dual-tier structure which employs the agent method. In the on-line process, a set of agents working simultaneously collect similar data points into sub-clusters by applying a heuristic strategy. And in the off-line process, summary information from the on-line component will be further analyzed to obtain the final clusters. The algorithm also supports the time-window queries on streams. The empirical evidence shows that this method can obtain high-quality clusters with low time complexity. analysis over an arbitrary period of the stream etc. As for stream clustering, a common method is dividing the streaming data into chunks, and algorithms for static sets can be used on each sub-set separately (2). In recent years, stream algorithms have developed into a two-phase structure (3), (4). Usually, a dual framework includes two parts: the on-line component and the off-line component. The former is responsible for the fast but rough processing of streaming data and saving the summary information to meet the one-pass restriction while the latter takes advantage of the information to conduct high-level analysis. At present, stream algorithms are still facing some problems, for example: sensitive to the initial data points; bad quality of clusters due to the loss of global information caused by dividing the stream; high time complexity etc. A novel dual-tier clustering algorithm for data streams, AGCluStream, is proposed in this paper. The on-line algorithm uses agents to make similar points denser in local areas, and record the temporary distribution of data according to the pyramidal time frame (3). The off-line algorithm uses these records to conduct time-window analysis and higher-level clustering analysis. AGCluStream dose not divide the stream, and it adopts an incomplete-partition strategy to maintain the global information more effectively.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133994111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The virtual human affective interaction based on affective entropy","authors":"Weimin Xue, Huimin Xue, Hong Bao","doi":"10.1109/GRC.2006.1635810","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635810","url":null,"abstract":"The affective virtual human (AFH) interaction is one of the hotspots in information science and life science today. This paper introduces the concept of affective space and entropy based on Markov stochastic model, presenting a new AFH interaction module based on Belief Desire Intention (BDI) agent. The affective interactive module (AIM) help virtual human to better understands people. The results of practical application indicate that virtual human plays up to human psychology. This method did provide initial way of developing and building affective model in human-machine interaction system and in computer animation, etc. Index Terms—virtual human, affective entropy, BDI agent, speech interaction.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117009555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dense rectangles in object-attribute data","authors":"R. Belohlávek, Vilém Vychodil","doi":"10.1109/GRC.2006.1635871","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635871","url":null,"abstract":"We study dense rectangles in data tables with binary attributes, i.e. subtables which are “almost full of 1’s”. Dense rectangles represent interesting patterns which an be thought of as particular granules in data tables. Rectangles which are “full of 1’s” appear as natural patterns in several areas and have been widely studied in computer science and data analysis. Our paper presents a study in which we loosen the criterion of a density of a rectangle. Instead of rectangles full of 1’s, we are interested in rectangles which may contain a few 0’s. This way, one can capture different kinds of patterns in data. These patterns elude methods which extract only rectangles “full of 1’s”. We propose several ways to define density of a rectangle. We concentrate on column-like (and dually, row-like) conditions which say that a rectangle is dense if each of its columns contains at most a given (small) number of 0’s. For this case, we develop theoretical insight resembling that one behind rectangles “full of 1’s”, present illustrative examples and experiments, and outline further issues and future research.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"371 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131075545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid SVM kernels for protein secondary structure prediction","authors":"Gulsah Altun, Hae-Jin Hu, D. Brinza, R. Harrison, A. Zelikovsky, Yi Pan","doi":"10.1109/GRC.2006.1635912","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635912","url":null,"abstract":"The Support Vector Machine is a powerful methodology for solving problems in nonlinear classification, function estimation and density estimation. When data are not linearly separable, data are mapped to a high dimensional future space using a nonlinear function which can be computed through a positive definite kernel in the input space. Using a suitable kernel function for a particular problem and input data can change the prediction results remarkably and improve the accuracy. The goal of this work is to find the best kernel functions that can be applied to different types of data and problems. In this paper, we propose two hybrid kernels SVMSM+RBF and SVMEDIT+RBF. SVMSM+RBF is designed by combining the best performed RBF kernel with substitution matrix (SM) based kernel developed by Vanschoenwinkel and Manderick. SVMEDIT+RBF kernel combines the RBF kernel and the edit kernel devised by Li and Jiang. We tested these two hybrid kernels on one of the widely studied problems in bioinformatics which is the protein secondary structure prediction problem. For the protein secondary structure problem, our results were 91% accuracy on H/E binary classifier.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134079889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}