A visual multiple criteria approach is presented with displacing limitations as well, as the features of the applied software system of MADMML, which is implementing it. That system has been applied in the field of material science to determine the technological modes providing the preset requirements to the values examined. It is investigated that non-dominated (effective) decisions are determined by applying various filters of the system
{"title":"Method for Solving Multiple Criteria Decision Making (MCDM) Problems and Decision Support System","authors":"N. Tontchev, S. Ivanov","doi":"10.1109/IS.2006.348405","DOIUrl":"https://doi.org/10.1109/IS.2006.348405","url":null,"abstract":"A visual multiple criteria approach is presented with displacing limitations as well, as the features of the applied software system of MADMML, which is implementing it. That system has been applied in the field of material science to determine the technological modes providing the preset requirements to the values examined. It is investigated that non-dominated (effective) decisions are determined by applying various filters of the system","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122997246","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}
Pub Date : 2006-09-01DOI: 10.1007/978-3-540-77623-9_17
M. Spott, Henry Abraham, D. Nauck
{"title":"Smart Data Analysis Services","authors":"M. Spott, Henry Abraham, D. Nauck","doi":"10.1007/978-3-540-77623-9_17","DOIUrl":"https://doi.org/10.1007/978-3-540-77623-9_17","url":null,"abstract":"","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114851948","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}
In this paper we analyze support vector machine classification using the soft margin approach that allows for errors and margin violations during the training stage. Two models for learning the separating hyperplane do exist. We study the behavior of the optimization algorithms in terms of training characteristics and test accuracy for unbalanced data sets. The main goal of our work is to compare the features of the resulting classification functions, which are mainly defined by the support vectors arising during the support vector machine training
{"title":"On the Advantages of Weighted L1-Norm Support Vector Learning for Unbalanced Binary Classification Problems","authors":"T. Eitrich, Bruno Lang","doi":"10.1109/IS.2006.348483","DOIUrl":"https://doi.org/10.1109/IS.2006.348483","url":null,"abstract":"In this paper we analyze support vector machine classification using the soft margin approach that allows for errors and margin violations during the training stage. Two models for learning the separating hyperplane do exist. We study the behavior of the optimization algorithms in terms of training characteristics and test accuracy for unbalanced data sets. The main goal of our work is to compare the features of the resulting classification functions, which are mainly defined by the support vectors arising during the support vector machine training","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114873986","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}
In this paper fuzzy logic (FL) and expert system (ES) theories are studied with regard to their contribution to solving the problem of OCR (optical character recognition). These theories have improved the learning and adaptation capacities related to varying shapes where information is qualitative, inaccurate or incomplete. The use of these technologies FL and ES proves interesting, efficient, and necessary to recognize all Arabic character. This combination is very useful to improve the powerful of hybrid intelligent systems HIS in the field of OCR. The primary goal of this combination (FL, ES) is to classify and to recognize all presented unknown shapes. These theories must achieve these tasks: to classify characters, and to make ones way of intelligent recognition by ES_FL system capturing the behaviour of a human expert knowledge. The training has used 280 descended pictures of the database of ACR (Arabic character recognition). The results gotten of ACR databases are promising
{"title":"The Combination of Fuzzy Logic and Expert System for Arabic Character Recognition","authors":"O. Hachour","doi":"10.1109/IS.2006.348415","DOIUrl":"https://doi.org/10.1109/IS.2006.348415","url":null,"abstract":"In this paper fuzzy logic (FL) and expert system (ES) theories are studied with regard to their contribution to solving the problem of OCR (optical character recognition). These theories have improved the learning and adaptation capacities related to varying shapes where information is qualitative, inaccurate or incomplete. The use of these technologies FL and ES proves interesting, efficient, and necessary to recognize all Arabic character. This combination is very useful to improve the powerful of hybrid intelligent systems HIS in the field of OCR. The primary goal of this combination (FL, ES) is to classify and to recognize all presented unknown shapes. These theories must achieve these tasks: to classify characters, and to make ones way of intelligent recognition by ES_FL system capturing the behaviour of a human expert knowledge. The training has used 280 descended pictures of the database of ACR (Arabic character recognition). The results gotten of ACR databases are promising","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130615497","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}
This paper presents a novel index, called I-Forest, to support data mining activities on evolving databases, whose content is periodically updated through insertion (or deletion) of data blocks. I-Forest allows the extraction of itemsets from transactional databases such as transactional data from large retail chains. Item, support and time constraints may be enforced during the extraction phase. The proposed index is a covering index that represents transactional blocks in a succinct form and allows different kinds of analysis (e.g., analyze quarterly data). During the creation phase no support constraint is enforced. Thus, the index provides a complete representation of the evolving data. The I-Forest index has been implemented Into the Post-greSQL open source DBMS and exploits its physical level access methods. Experiments have been run for both sparse and dense data distributions. The execution time of the frequent itemset extraction task exploiting the index is always comparable with and for low support threshold faster than the Prefix-Tree algorithm accessing static data on at file
{"title":"Itemset Mining on Indexed Data Blocks","authors":"Elena Baralis, T. Cerquitelli, S. Chiusano","doi":"10.1109/IS.2006.348526","DOIUrl":"https://doi.org/10.1109/IS.2006.348526","url":null,"abstract":"This paper presents a novel index, called I-Forest, to support data mining activities on evolving databases, whose content is periodically updated through insertion (or deletion) of data blocks. I-Forest allows the extraction of itemsets from transactional databases such as transactional data from large retail chains. Item, support and time constraints may be enforced during the extraction phase. The proposed index is a covering index that represents transactional blocks in a succinct form and allows different kinds of analysis (e.g., analyze quarterly data). During the creation phase no support constraint is enforced. Thus, the index provides a complete representation of the evolving data. The I-Forest index has been implemented Into the Post-greSQL open source DBMS and exploits its physical level access methods. Experiments have been run for both sparse and dense data distributions. The execution time of the frequent itemset extraction task exploiting the index is always comparable with and for low support threshold faster than the Prefix-Tree algorithm accessing static data on at file","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128128201","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}
K. Tenekedjiev, N. Nikolova, C. A. Kobashikawa, K. Hirota
The paper discusses rational conservative betting on sport game events by a fuzzy (partially rational) decision maker with the help of generalized lotteries of II type. The scheme accounts for the interval character of probability elicitation results, which may be conveniently described by intuitionistic fuzzy sets. A model of a lottery with intuitionistic fuzzy representation of the state uncertainty is proposed, called fuzzy rational lottery. Utility theory is not directly applicable to that type of lotteries, which is why two transformations into ordinary lotteries are proposed - classical and conservative. The classical set-up uses point estimates of the probability uncertainty intervals to construct lotteries, whereas the conservative set-up is a combination of Wald's maximin principle and utility theory under risk. Those approaches are applied to analyze a hypothetical betting situation over the results of soccer game. Betting on single events, as well as simultaneously on all events is discussed, and conditions are found for optimal betting
{"title":"Conservative Betting on Sport Games with Intuitionistic Fuzzy Described Uncertainty","authors":"K. Tenekedjiev, N. Nikolova, C. A. Kobashikawa, K. Hirota","doi":"10.1109/IS.2006.348514","DOIUrl":"https://doi.org/10.1109/IS.2006.348514","url":null,"abstract":"The paper discusses rational conservative betting on sport game events by a fuzzy (partially rational) decision maker with the help of generalized lotteries of II type. The scheme accounts for the interval character of probability elicitation results, which may be conveniently described by intuitionistic fuzzy sets. A model of a lottery with intuitionistic fuzzy representation of the state uncertainty is proposed, called fuzzy rational lottery. Utility theory is not directly applicable to that type of lotteries, which is why two transformations into ordinary lotteries are proposed - classical and conservative. The classical set-up uses point estimates of the probability uncertainty intervals to construct lotteries, whereas the conservative set-up is a combination of Wald's maximin principle and utility theory under risk. Those approaches are applied to analyze a hypothetical betting situation over the results of soccer game. Betting on single events, as well as simultaneously on all events is discussed, and conditions are found for optimal betting","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125545694","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}
The problem of identification consists of setting up a suitably parameterized identification model and adjusting the parameters of the model by optimizing a performance index. Parallel and parallel-series identification methods are used to adjust an unknown model's parameters. In this paper a combined parallel/series-parallel identification model, based on TSK fuzzy model and wavelet neuro-fuzzy model, is proposed
{"title":"Identification and Prediction of Nonlinear Dynamical Plants Using TSK and Wavelet Neuro-Fuzzy Models","authors":"A. Banakar, M. Azeem","doi":"10.1109/IS.2006.348490","DOIUrl":"https://doi.org/10.1109/IS.2006.348490","url":null,"abstract":"The problem of identification consists of setting up a suitably parameterized identification model and adjusting the parameters of the model by optimizing a performance index. Parallel and parallel-series identification methods are used to adjust an unknown model's parameters. In this paper a combined parallel/series-parallel identification model, based on TSK fuzzy model and wavelet neuro-fuzzy model, is proposed","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123089755","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}
This paper presents a fuzzy based bandwidth allocation method by using bandwidth borrowing scheme for multimedia wireless networks in the context of future generation cellular networks. In the proposed scheme, bandwidth is borrowed from the applications that are already running in a cell for the new/handoff calls based on the fuzzy parameters such as application priority, age of the connection and bandwidth allocated during the connection time. The scheme guarantees that no connection gives up more than its fair share of bandwidth. The scheme has been extensively simulated for its operation effectiveness. The simulation results show that fuzzy based bandwidth borrowing scheme performs better than the traditional rate borrowing scheme in terms of call dropping, call rejection and bandwidth utilization
{"title":"Bandwidth Allocation For Wireless Multimedia Traffic By Using Fuzzy Logic","authors":"J. Mallapur, S. Manvi, D. H. Rao","doi":"10.1109/IS.2006.348426","DOIUrl":"https://doi.org/10.1109/IS.2006.348426","url":null,"abstract":"This paper presents a fuzzy based bandwidth allocation method by using bandwidth borrowing scheme for multimedia wireless networks in the context of future generation cellular networks. In the proposed scheme, bandwidth is borrowed from the applications that are already running in a cell for the new/handoff calls based on the fuzzy parameters such as application priority, age of the connection and bandwidth allocated during the connection time. The scheme guarantees that no connection gives up more than its fair share of bandwidth. The scheme has been extensively simulated for its operation effectiveness. The simulation results show that fuzzy based bandwidth borrowing scheme performs better than the traditional rate borrowing scheme in terms of call dropping, call rejection and bandwidth utilization","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134418316","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}
In this paper, a simple and effective scheme for tuning of fuzzy PI (proportional-integral) controller based on fuzzy logic is proposed. Here the input scaling factors are tuned online by gain updating factors whose values are determined by rule base with the error and change in error as inputs according to the required controlled process. The performance comparison of conventional fuzzy logic controller with auto tuned fuzzy PI type controllers has been done in terms of several performance measures such as peak overshoot, settling time and rise time and integral square error (ISE). In addition to the responses due to step set-point change, a random noise is also added in some systems. Simulation results show the effectiveness and robustness of the proposed tuning mechanism. Furthermore, a clustering method is used to reduce the fuzzy inference rules of the three fuzzy reasoning blocks which reduces the computational time and memory. The clustering based fuzzy logic controllers is compared with those of conventional fuzzy logic controllers in both cases (with and without tuning). A simulation analysis of a wide range of linear and nonlinear processes is carried out and comparison of results shows computational time and memory is reduced to a great extent
{"title":"A Robust Scheme for Tuning of Fuzzy PI Type Controller","authors":"S. Chopra, R. Mitra, V. Kumar","doi":"10.1109/IS.2006.348435","DOIUrl":"https://doi.org/10.1109/IS.2006.348435","url":null,"abstract":"In this paper, a simple and effective scheme for tuning of fuzzy PI (proportional-integral) controller based on fuzzy logic is proposed. Here the input scaling factors are tuned online by gain updating factors whose values are determined by rule base with the error and change in error as inputs according to the required controlled process. The performance comparison of conventional fuzzy logic controller with auto tuned fuzzy PI type controllers has been done in terms of several performance measures such as peak overshoot, settling time and rise time and integral square error (ISE). In addition to the responses due to step set-point change, a random noise is also added in some systems. Simulation results show the effectiveness and robustness of the proposed tuning mechanism. Furthermore, a clustering method is used to reduce the fuzzy inference rules of the three fuzzy reasoning blocks which reduces the computational time and memory. The clustering based fuzzy logic controllers is compared with those of conventional fuzzy logic controllers in both cases (with and without tuning). A simulation analysis of a wide range of linear and nonlinear processes is carried out and comparison of results shows computational time and memory is reduced to a great extent","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130285362","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}
P. G. Kulkarni, Sally McClean, Gerard Parr, Michaela Black
Due to increasing reliance on computer communication networks, it is highly desirable that networks should have the ability to detect symptoms of oncoming exception conditions and take measures to prevent them thereby enabling a degree of proactive network management that underpins an acceptable quality of service. This paper proposes a framework for achieving congestion avoidance through proactive network management using data mining. It examines the inter-relationships between network element management information base (MIB) attributes, queue parameters (associated with a transmission link) and the level of congestion at a network node and identifies hybrid parameters that have a bearing on congestion. By employing data mining on the data pertaining to these variables, congestion at the network node can be predicted. Results from our initial experimentation with particular data mining models show that the accuracy achieved is as high as 98% in all of the cases thus rendering data mining a viable approach to proactively identity network exception conditions
{"title":"Deploying MIB Data Mining for Proactive Network Management","authors":"P. G. Kulkarni, Sally McClean, Gerard Parr, Michaela Black","doi":"10.1109/IS.2006.348471","DOIUrl":"https://doi.org/10.1109/IS.2006.348471","url":null,"abstract":"Due to increasing reliance on computer communication networks, it is highly desirable that networks should have the ability to detect symptoms of oncoming exception conditions and take measures to prevent them thereby enabling a degree of proactive network management that underpins an acceptable quality of service. This paper proposes a framework for achieving congestion avoidance through proactive network management using data mining. It examines the inter-relationships between network element management information base (MIB) attributes, queue parameters (associated with a transmission link) and the level of congestion at a network node and identifies hybrid parameters that have a bearing on congestion. By employing data mining on the data pertaining to these variables, congestion at the network node can be predicted. Results from our initial experimentation with particular data mining models show that the accuracy achieved is as high as 98% in all of the cases thus rendering data mining a viable approach to proactively identity network exception conditions","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"69 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130386753","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}
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