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2006 IEEE International Conference on Granular Computing最新文献

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The roots of granular computing 颗粒计算的根源
2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635922
A. Bargiela, W. Pedrycz
{"title":"The roots of granular computing","authors":"A. Bargiela, W. Pedrycz","doi":"10.1109/GRC.2006.1635922","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635922","url":null,"abstract":"Granular Computing arose as a synthesis of insights into human-centred information processing by Zadeh in the late '90s and the Granular Computing name was coined, at this early stage, by T.Y Lin. Although the name is now in widespread use, or perhaps because of it, there are calls for a clarification of the distinctiveness of Granular Computing against the background of other human-centred information processing paradigms. This study examines the basic motivation for information granulation and casts Granular Computing as a structured combination of algorithmic and non-algorithmic information processing that mimics human, intelligent synthesis of knowledge from information.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"13 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":"114388696","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}
引用次数: 118
Multi-objective evolutionary algorithms and rough sets for decomposition and analysis of cortical evoked potentials 皮层诱发电位分解与分析的多目标进化算法与粗糙集
2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635882
M. Milanova, T. Smolinski, G. Boratyn, R. Buchanan, A. Prinz
{"title":"Multi-objective evolutionary algorithms and rough sets for decomposition and analysis of cortical evoked potentials","authors":"M. Milanova, T. Smolinski, G. Boratyn, R. Buchanan, A. Prinz","doi":"10.1109/GRC.2006.1635882","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635882","url":null,"abstract":"Signal decomposition techniques prove to be useful in the analysis of neural activity, as they allow for identification of supposedly distinct neuronal structures (i.e., sources of activity). Applied to measurements of brain activity in a controlled setting as well as under exposure to an external stimulus, they allow for analysis of the impact of the stimulus on those structures. The link between the stimulus and a given source can be confirmed by a classifier that is able to \"predict\" if a given signal was registered under one or the other condition, solely based on the components. Very often, however, statistical criteria used in traditional decomposition techniques turn out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel hybrid technique based on multi-objective evolutionary algorithms (MOEA) and rough sets (RS) that will perform decomposition in the light of the classification problem itself.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"24 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":"134647318","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}
引用次数: 7
Applying RBF Network to Predict Location in Mobile Network 应用RBF网络预测移动网络中的位置
2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635835
Ming Lei, Pilian He, Zhichao Li
{"title":"Applying RBF Network to Predict Location in Mobile Network","authors":"Ming Lei, Pilian He, Zhichao Li","doi":"10.1109/GRC.2006.1635835","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635835","url":null,"abstract":"In mobile network, quality of service (Qos) is difficultly guaranteed for the particularity of mobile network. If the system knows, prior to the mobile subscriber movement, the exact trajectory it will follow, the Qos can be guaranteed. Thus, location prediction is the key issue to provide quality of service to mobile subscriber. In the present paper, RBF Network of Neural Network techniques were used to predict the mobile user's next location based on his current location as well as time. The software matlab 6.5 was used to confirm the parameters of RBF network, and to same training data, makes the detailed contrast with resilient propagation BP and BP in learning time and steps of learning. Experiment results show that predicted locations with RBF are more effective and accurate than resilient BP.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"69 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":"132793331","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}
引用次数: 0
Pseudo-quotient space theory for granular computing of non-partition model 非分割模型颗粒计算的伪商空间理论
2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635804
Wan-Li Chen, Qian-Sheng Fang, Jia-xing Cheng
{"title":"Pseudo-quotient space theory for granular computing of non-partition model","authors":"Wan-Li Chen, Qian-Sheng Fang, Jia-xing Cheng","doi":"10.1109/GRC.2006.1635804","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635804","url":null,"abstract":"Quotient space theory, a general theoretical model of granular computing, is generalized from the viewpoint of granulation. Reflexive and symmetric binary relation, namely compatibility relation, plays the role of granulation. By the construction of homeomorphism, it is proved that most of conclusions of classical quotient space theory keep being valid. The consequent model is called pseudo-quotient space theory. To some degree, the generalization enriches contents of quotient space theory and makes it possible to solve problems of non-partition model.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"70 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":"123431955","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}
引用次数: 0
Grid intrusion detection based on soft computing by modeling real-user's normal behaviors 基于软计算的网格入侵检测，对真实用户的正常行为进行建模
2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635864
Guiling Zhang, Ji-zhou Sun
{"title":"Grid intrusion detection based on soft computing by modeling real-user's normal behaviors","authors":"Guiling Zhang, Ji-zhou Sun","doi":"10.1109/GRC.2006.1635864","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635864","url":null,"abstract":"This paper proposes a novel structure of GRID intrusion detection system based on distributed intelligent agents and soft computing techniques (SCGIDS). The SCGIDS models each real-user's normal behaviors and saves the real-user's normal behavior description parameters to a specific database. The on-line real-user's behaviors are then evaluated by a soft computing system with these saved normal behavior description parameters; if the deviation is exceed a specific value, the intrusion may appear. Additionally, the proposed SCGIDS has the ability of self-learning. When the on-line real-user's normal behavior excursion is in an allowed extent, the parameters of the corresponding real-user's normal behavior description parameters are adjusted automatically. More advantages of the SCGIDS are that it has simple intrusion trace-back method and the intrusion evidences for the law can be collected very easily. The soft computing based SCGIDS consists of the SOM (self-organize map) dimension reduction technique, the novel fuzzy neural network and an improved genetic algorithm. The key components are simulated in the LINUX with Globus 2.1. The prototype experimental results show that the proposed SCGIDS is a very accurate system for GRID intrusion detection.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"5 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":"121948284","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}
引用次数: 4
Dynamic information system and its rough set model based on time sequence 基于时间序列的动态信息系统及其粗糙集模型
2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635860
Xiaowei He, Liming Xu, Wenzhong Shen
{"title":"Dynamic information system and its rough set model based on time sequence","authors":"Xiaowei He, Liming Xu, Wenzhong Shen","doi":"10.1109/GRC.2006.1635860","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635860","url":null,"abstract":"Uncertain problems in fields of economy, population, medical treatment and real estate always present the extraordinary nature of inconsistency and process. With the aim to adapt traditional rough set theory and put it into dynamic information system, this paper first constructs dynamic information system based on time sequence and relevant rough set model, on the circumstances of keeping universe and attribute set intact. Secondly, it presents some properties of equivalent class and rough set based on time sequence during the alterative process of information system. Thus, this paper paves the way for further research on the theories and applications of rough set based on dynamic information systems. Index Terms—Rough Set, Time Sequence, Dynamic Information System","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"7 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":"127380491","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}
引用次数: 4
Automatic acquisition of concepts from domain texts 从领域文本中自动获取概念
2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635831
Janardhana Punuru, Jianhua Chen
{"title":"Automatic acquisition of concepts from domain texts","authors":"Janardhana Punuru, Jianhua Chen","doi":"10.1109/GRC.2006.1635831","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635831","url":null,"abstract":"Domain specific concept extraction is a key com- ponent in ontology construction for Semantic Web applications. Manual concept extraction is costly both in time and labor. In this paper, we present several heuristic methods for automatic concepts extraction from domain texts. These methods aim to improve the precision and recall over the word frequency-based techniques. Precision is improved by elimination of irrelevant terms using word sense information. Recall is enhanced by adding new concepts formed by composition of relevant words. Our methods are domain independent, and can be applied in fully automatic way to the concept extraction task. Experimental results on the electronic voting domain texts (from New York Times) are presented which show the promise of the proposed methods. Index Terms— Concept extraction, ontology engineering, text processing, WordNet, WordNet Senses.","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":"129442562","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}
引用次数: 8
A new optimization method based on restructuring penalty function for solving constrained minimization problems 一种基于重构惩罚函数的约束最小化问题优化方法
2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635852
L. Zijun, Baiquan Lu, Yuan Cao
{"title":"A new optimization method based on restructuring penalty function for solving constrained minimization problems","authors":"L. Zijun, Baiquan Lu, Yuan Cao","doi":"10.1109/GRC.2006.1635852","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635852","url":null,"abstract":"In this paper, a new optimization method is presented for solving the constrained minimization problems under a weak Mangasarian-Fromovit's regularity condition, Generally, there are two methods for getting all points satisfying the Kuhn-Tucker conditions. The first one is to use different initial points for a given penalty function to find the attraction region of each optimization solution. Another is to use different penalty functions to find the attraction regions of each Kuhn-Tucker point in turn to get the corresponding points satisfying the Kuhn-Tucker conditions. In this paper, a hybrid convergence algorithm based on the two methods is given for solving constrained minimization problems in which a solution of new penalty function based on the obtained Kuhn-Tucker points converges to a new Kuhn-Tucker point if it exists, thus new Kuhn-Tucker point is got continuously by different penalty function until all Kuhn-Tucker points are got. Numerical examples are provided to demonstrate its effectiveness and applicability.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"18 25","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120965905","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}
引用次数: 0
Granular computing II: Infrastructures for AI-Engineering 颗粒计算II:人工智能工程的基础设施
2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635743
T. Lin
{"title":"Granular computing II: Infrastructures for AI-Engineering","authors":"T. Lin","doi":"10.1109/GRC.2006.1635743","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635743","url":null,"abstract":"What is granular computing? There are no well accepted formal definitions yet. Informally, any computing theory/technology that involves elements and granules (subsets or generalized subsets) may be called granular computing (GrC). Intuitively, elements are the data, and granules are the basic knowledge. So granular computing is the infrastructures for AIEngineering: uncertainty management, data mining, knowledge engineering, and learning.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"16 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":"128726531","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}
引用次数: 17
Spam email filtering with bayesian belief network: using relevant words 基于贝叶斯信念网络的垃圾邮件过滤:使用相关词
2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635790
X. Jin, Anbang Xu, R. Bie, Xian Shen, Min Yin
{"title":"Spam email filtering with bayesian belief network: using relevant words","authors":"X. Jin, Anbang Xu, R. Bie, Xian Shen, Min Yin","doi":"10.1109/GRC.2006.1635790","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635790","url":null,"abstract":"In this paper, we report our work on a Bayesian Belief Network approach to spam email filtering (classifying email as spam or nonspam/legitimate). Our evaluation suggests that a Bayesian Belief Network based classifier will outperform the popular Naive Bayes approach and two other famous learners: decision tree and k-NN. These four algorithms are tested on two different data sets with three different feature selection methods (Information Gain, Gain Ratio and Chi Squared) for finding relevant words. 10-fold cross-validation results show that Bayesian Belief Network performs best on both datasets. We suggest that this is because the 'dependant learner' characteristics of Bayesian Belief Network classification are more suited to spam filtering. The performance of the Bayesian Belief Network classifier could be further improved by careful feature subset selection.","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":"131306348","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}
引用次数: 9
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