2006 IEEE International Conference on Granular Computing最新文献

{"title":"Lie group machine learning's axiom hypothesizes","authors":"Huan Xu, Fanzhang Li","doi":"10.1109/GRC.2006.1635825","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635825","url":null,"abstract":"profound inherent theory. [5] It just can meet the needs of machine learning and describe the procedure of machine learning clearly. So Lie group machine learning is formed. This paper is based on the basic conceptions of machine learning and gives the generalization hypothesis axiom; the partition independence hypothesis axiom; the duality hypothesis axiom and the learning compatibility hypothesis axiom of Lie group machine learning. Index terms—Lie group; machine learning; Lie group machine leaning; hypothesis axiom","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"6 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":"122179668","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":"Evolutionary voting kernel machines for cyclooxygenase-2 inhibitor activity comparisons","authors":"Bo Jin, Yanqing Zhang","doi":"10.1109/GRC.2006.1635891","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635891","url":null,"abstract":"With the growing interest of biological data prediction and chemical data prediction, more complicated kernels are designed to measure data similarities. In (1), we proposed a kind of evolutionary granular kernel trees (EGKTs) for drug activity comparisons. In EGKTs, feature granules and tree structures are predefined based on the possible substituent locations. In (2), we proposed a granular kernel tree structure evolving system (GKTSES) to evolve the structures of GKTs in the case that we lack knowledge to predefine kernel trees. In this paper, evolutionary voting kernel machines (EVKMs) are presented based on GKTSES. Experimental results show that EVKMs are more stable than GKTSES in cyclooxygenase-2 inhibitor activity comparisons. Index Terms—Drug activity comparisons, kernel, genetic algorithms, granular kernel trees, support vector machines.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"29 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":"114237489","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":"Default assumption reasoning based on DFL","authors":"Jin Huang, Fanzhang Li","doi":"10.1109/GRC.2006.1635828","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635828","url":null,"abstract":"Mankind can use uncertain and incomplete information to infer. And the conclusions are always satisfying. In our daily life most objects in the reasoning processing have Dynamic Fuzzy character. Due to this we propose default assumption reasoning based on DFL. Here, we introduce the reasoning frame, Dynamic Fuzzy knowledge representation, reasoning algorithms and so on.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"48 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":"116767636","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":"Multiview intelligent data analysis based on granular computing","authors":"Yaohua Chen, Yiyu Yao","doi":"10.1109/GRC.2006.1635797","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635797","url":null,"abstract":"Multiview intelligent data analysis explores data from different perspectives to reveal various types of structures and knowledge embedded in the data. Granular computing provides a general methodology for problem solving and information processing. Its application to data analysis results in hierarchical knowledge structures. In this paper, the funda- mental issues of granulations and granular structures for data analysis are discussed based on modal-style data operators. The results provide a basis for establishing a framework of multiview intelligent data analysis.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"458 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":"116772319","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":"Ensembles of classifiers based on rough sets theory and set-oriented database operations","authors":"Xiaohua Hu","doi":"10.1109/GRC.2006.1635760","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635760","url":null,"abstract":"In this paper we present a new approach to construct a good ensemble of classifiers for data mining applications based on rough set theory and database set operations. We borrow the main ideas of rough set theory and redefine them based on the database theory to take advantage of the very efficient set-oriented database operation. Our method first computes a set of reducts which include all the necessary attributes required for the decision categories. For each reduct, a reduct table is generated by removing those attributes which are not in the reduct. Next a novel rule induction algorithm is used to compute the maximal generalized rules for each reduct table and a set of reduct classifiers is formed based on the corresponding reducts. Our rule induction algorithm adopts the \"conquer-without-separating \" strategy to generate a set of global best rules from the data set. The experimental results indicates that the rough set based approach is very promising for ensemble of classifiers.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"22 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":"125077087","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 taxonomy of types of granularity","authors":"C. Keet","doi":"10.1109/GRC.2006.1635767","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635767","url":null,"abstract":"Multiple different understandings and uses exist of what granularity is and how to implement it, where the former influences success of the latter with regards to storing granular data and using granularity for reasoning over the data or information. We propose a taxonomy of types of granularity and discuss for each leaf type how the entities or instances relate within its granular level. Such unambiguous distinctions can guide a conceptual modeler to better distinguish between the types of granularity and the software developer to improve on implementations of granularity.","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":"131142776","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":"Combating imbalance in network intrusion datasets","authors":"David A. Cieslak, N. Chawla, A. Striegel","doi":"10.1109/GRC.2006.1635905","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635905","url":null,"abstract":"An approach to combating network intrusion is the development of systems applying machine learning and data min- ing techniques. Many IDS (Intrusion Detection Systems) suffer from a high rate of false alarms and missed intrusions. We want to be able to improve the intrusion detection rate at a reduced false positive rate. The focus of this paper is rule-learning, using RIPPER, on highly imbalanced intrusion datasets with an objective to improve the true positive rate (intrusions) without significantly increasing the false positives. We use RIPPER as the underlying rule classifier. To counter imbalance in data, we implement a combination of oversampling (both by replication and synthetic generation) and undersampling techniques. We also propose a clustering based methodology for oversampling by generating synthetic instances. We evaluate our approaches on two intrusion datasets — destination and actual packets based — constructed from actual Notre Dame traffic, giving a flavor of real-world data with its idiosyncrasies. Using ROC analysis, we show that oversampling by synthetic generation of minority (intrusion) class outperforms oversampling by replication and RIPPER's loss ratio method. Additionally, we establish that our clustering based approach is more suitable for the detecting intrusions and is able to provide additional improvement over just synthetic generation of instances.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"72 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":"134222737","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 framework of linguistic truth-valued propositional logic based on lattice implication algebra","authors":"L. Zou, Jun Ma, Yang Xu","doi":"10.1109/GRC.2006.1635868","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635868","url":null,"abstract":"The linguistic truth values with linguistic hedges is considered. The linguistic hedge operators in the proposition are put forward and the truth values are divided into different grades. Based on lattice implication algebra a framework of linguistic truth-valued propositional logic is presented to deal with both comparable and incomparable of linguistic truth value. The properties of the propositional formula are discussed. Then based on a filter J of L, J-true, J-false of a formula, J-similar literals and J-complementary literals are defined. In the filter, J-resolution method of the linguistic truth value propositional logic is presented.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"2 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":"115545096","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":"Towards the integration of diverse spam filtering techniques","authors":"C. Pu, Steve Webb, Oleg M. Kolesnikov, Wenke Lee, R. Lipton","doi":"10.1109/GRC.2006.1635746","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635746","url":null,"abstract":"Text-based spam filters (e.g., keyword and statistical learning filters) use tokens, which are found during message content analysis, to separate spam from legitimate messages. The effectiveness of these token-based filters is due to the presence of token signatures (i.e., tokens that are invariant for the many variants of spam messages). Unfortunately, it is relatively easy for spammers to hide or erase these signatures through simple techniques such as misspellings (to confuse keyword filters) and camouflage (i.e., combined spam and legitimate content used to confuse statistical filters). Our hypothesis is that spam contains additional signatures which are more difficult to hide. A concrete example of this type of signature is the presence of URLs in spam messages which are used to induce contact from their victims. We believe diverse spam filtering tools should be developed to incorporate these additional signatures. Thus, in this paper, we discuss a new type of URL-based filtering which can be integrated with existing spam filtering techniques to provide a more robust anti-spam solution. Our approach uses the syntactic constraints of URLs to find them in emails, and then, it uses semantic knowledge and tools (e.g., search engines) to refine and sharpen the spam identification process. email's routed path. In this paper, we focus our attention on spam messages that contain URLs and provide a novel approach for filtering these messages. The key observation is that most spam messages contain URLs which are \"live\" since the spammers would not be able to profit without a functioning link to their site. Thus, by checking the URLs found in a message and verifying a user's interest in the websites referenced by those URLs, we are able to add a new dimension to spam filtering. This paper has two main contributions. First, we describe three techniques for filtering email messages that contain URLs: URL category whitelists, URL regular expression whitelists, and dynamic classification of websites. Second, we describe a prototype implementation that takes advantage of these three techniques to help enhance spam filtering. Our pre- liminary results suggest that new dimensions in spam filtering (e.g., using URLs) deserve further exploration. However, due to space limitations, we have omitted our experimental results from this paper. The remainder of the paper is structured as follows. Sec- tion II gives an overview of the related work done in this research area. In Section III, we describe our approach, and Section IV discusses the details of our system's implementa- tion. We provide our conclusions in Section V.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"82 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":"122624466","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 framework of temporal granular logic based on information system","authors":"Xiaoqing Chen, Taorong Qiu, Qing Liu, Houkuan Huang","doi":"10.1109/GRC.2006.1635875","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635875","url":null,"abstract":"Based on information system this paper presents a general framework of first order temporal granular logic and defines the meaning set of a formula .the paper provide axiom schemes and deductive rules based on which logic methodology or set theory can be applied in deduction. v a Its meaning set","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"14 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":"125304068","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}