Pub Date : 2009-09-22DOI: 10.1109/GRC.2009.5255052
Ming-Fen Wu, Ting-Liang Wang
Calculating the core of a decision information system is the start of information reduction and a key step of decision rule making. In this paper, we analyze essential characters of core attributes of decision information system according to rough set theory. Then researching the relationship between discernibility matrix' single attribute element and a core attribute. As algorithms, which were given out by Skowron and Zhang, has highly computing complicacy for calculating the core of decision system based on discernibility matrix. This paper gives out an improved algorithm, and proves it to be right. The simulation experiments shows that the new algorithm's calculating work will be reduced according to the proportion of inconsistent objects has risen.
{"title":"Characterization and algorithm of decision system's core based discernibility matrix","authors":"Ming-Fen Wu, Ting-Liang Wang","doi":"10.1109/GRC.2009.5255052","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255052","url":null,"abstract":"Calculating the core of a decision information system is the start of information reduction and a key step of decision rule making. In this paper, we analyze essential characters of core attributes of decision information system according to rough set theory. Then researching the relationship between discernibility matrix' single attribute element and a core attribute. As algorithms, which were given out by Skowron and Zhang, has highly computing complicacy for calculating the core of decision system based on discernibility matrix. This paper gives out an improved algorithm, and proves it to be right. The simulation experiments shows that the new algorithm's calculating work will be reduced according to the proportion of inconsistent objects has risen.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130921541","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255046
Bizhou Xiong, Benting Wan
Nondedicated distributed system is composed of many computers. Every computer has its owner user who has the highest priority using the computer. Special attention must be paid to the utilizing ratio of computer resource while computing tasks are allocated to distributed system. A relative queue model is constructed in this paper which is used to process data with synchronous relationship among them and perform data collection in multicomputer distributed system so we can calculate not only the resource utilizing ratio of individual computer but also the resource utilizing ratio of the whole system. Consequently, it can perform dynamitic and real-time monitoring on distributed system resource utilizing ratio properly and conveniently. After implementing the model in a distributed system and comparing the implementation results with performance monitor in Window 2000, which is task manager, the results indicate that relative queue model proposed in this paper can real-timely and dynamically monitor multi-computer distributed system performance satisfactorily.
{"title":"Relative queue-based distributed system performance real-time dynamic monitor","authors":"Bizhou Xiong, Benting Wan","doi":"10.1109/GRC.2009.5255046","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255046","url":null,"abstract":"Nondedicated distributed system is composed of many computers. Every computer has its owner user who has the highest priority using the computer. Special attention must be paid to the utilizing ratio of computer resource while computing tasks are allocated to distributed system. A relative queue model is constructed in this paper which is used to process data with synchronous relationship among them and perform data collection in multicomputer distributed system so we can calculate not only the resource utilizing ratio of individual computer but also the resource utilizing ratio of the whole system. Consequently, it can perform dynamitic and real-time monitoring on distributed system resource utilizing ratio properly and conveniently. After implementing the model in a distributed system and comparing the implementation results with performance monitor in Window 2000, which is task manager, the results indicate that relative queue model proposed in this paper can real-timely and dynamically monitor multi-computer distributed system performance satisfactorily.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129580531","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255071
Zhongzhi Shi, Zuqiang Meng, Yuan Lu
At present GrC mainly is divided into three categories: computing with words (CW), rough set (RS) and quotient space (QS). From the perspective of essential characteristic of GrC, this demarcation is not comprehensive and accurate. In fact, CW is based on fuzzy granules (fuzzy subset) and both RS and QS are based on disjoint granules, which essentially are equivalence classes, either equal to each other or with empty overlap. In practical application, intersecting granules however need to be handled. Therefore there is another kind of GrC which is based on intersecting granules. This kind of GrC is referred to as tolerance GrC (TGrC) in our work. By constructing a Boolean algebra on super-granular space and a decision algebraic system, this paper will present an incomplete information system-based TGrC. With the TGrC, an example about extracting rules incomplete information is given, so as to show its basic principle.
{"title":"Tolerance Granular Computing based on incomplete information system","authors":"Zhongzhi Shi, Zuqiang Meng, Yuan Lu","doi":"10.1109/GRC.2009.5255071","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255071","url":null,"abstract":"At present GrC mainly is divided into three categories: computing with words (CW), rough set (RS) and quotient space (QS). From the perspective of essential characteristic of GrC, this demarcation is not comprehensive and accurate. In fact, CW is based on fuzzy granules (fuzzy subset) and both RS and QS are based on disjoint granules, which essentially are equivalence classes, either equal to each other or with empty overlap. In practical application, intersecting granules however need to be handled. Therefore there is another kind of GrC which is based on intersecting granules. This kind of GrC is referred to as tolerance GrC (TGrC) in our work. By constructing a Boolean algebra on super-granular space and a decision algebraic system, this paper will present an incomplete information system-based TGrC. With the TGrC, an example about extracting rules incomplete information is given, so as to show its basic principle.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129590349","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255034
Xibei Yang, Jing-yu Yang, Xiaohua Hu
Rough set models based on the tolerance and similarity relations, are constructed to deal with incomplete information systems. Unfortunately, tolerance and similarity relations have their own limitations because the former is too loose while the latter is too strict in classification analysis. To make a reasonable and flexible classification in incomplete information system, a new binary relation is proposed in this paper. This new binary relation is only reflective and it is a generalization of tolerance and similarity relations. Furthermore, three different rough set models based on the above three different binary relations are compared and then some important properties are obtained. Finally, the direct approach to certain and possible rules induction in incomplete information system is investigated, an illustrative example is analyzed to substantiate the conceptual arguments.
{"title":"A new rough set model for knowledge acquisition in incomplete information system","authors":"Xibei Yang, Jing-yu Yang, Xiaohua Hu","doi":"10.1109/GRC.2009.5255034","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255034","url":null,"abstract":"Rough set models based on the tolerance and similarity relations, are constructed to deal with incomplete information systems. Unfortunately, tolerance and similarity relations have their own limitations because the former is too loose while the latter is too strict in classification analysis. To make a reasonable and flexible classification in incomplete information system, a new binary relation is proposed in this paper. This new binary relation is only reflective and it is a generalization of tolerance and similarity relations. Furthermore, three different rough set models based on the above three different binary relations are compared and then some important properties are obtained. Finally, the direct approach to certain and possible rules induction in incomplete information system is investigated, an illustrative example is analyzed to substantiate the conceptual arguments.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128256698","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255066
Jieli Sun, Zhiqing Zhu, Y. Wang
This paper studies Personalized recommendation adaptive dynamic case expression according to the characteristics of the method of case-based reasoning (CBR) and the personal recommendation cases. And the design thought of the personalized recommendation adaptive dynamic case expression are discussed. Finally, the design methods of the personalized recommendation adaptive dynamic case expression are analyzed.
{"title":"Research on Personalized recommendation adaptive dynamic case expression","authors":"Jieli Sun, Zhiqing Zhu, Y. Wang","doi":"10.1109/GRC.2009.5255066","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255066","url":null,"abstract":"This paper studies Personalized recommendation adaptive dynamic case expression according to the characteristics of the method of case-based reasoning (CBR) and the personal recommendation cases. And the design thought of the personalized recommendation adaptive dynamic case expression are discussed. Finally, the design methods of the personalized recommendation adaptive dynamic case expression are analyzed.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128410816","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255159
Hongmei Chen, Tianrui Li, Weibin Liu, Weili Zou
In rough set theory (RST), upper and lower approximations of a concept will change dynamically while the information system varies over time. How to update approximations based on the original approximations' information is an important problem since it may improve the efficiency of knowledge discovery. This paper focuses on the approach for dynamically updating approximations when attribute values coarsening or refining. The definitions of attribute values coarsening and refining in information systems are introduced. The properties for dynamic maintenance of upper and lower approximations while attribute values coarsen and refine are presented. Finally, the principle of coarsening or refining of the multi-granularity attribute values is analyzed.
{"title":"Research on the approach of dynamically maintenance of approximations in rough set theory while attribute values coarsening and refining","authors":"Hongmei Chen, Tianrui Li, Weibin Liu, Weili Zou","doi":"10.1109/GRC.2009.5255159","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255159","url":null,"abstract":"In rough set theory (RST), upper and lower approximations of a concept will change dynamically while the information system varies over time. How to update approximations based on the original approximations' information is an important problem since it may improve the efficiency of knowledge discovery. This paper focuses on the approach for dynamically updating approximations when attribute values coarsening or refining. The definitions of attribute values coarsening and refining in information systems are introduced. The properties for dynamic maintenance of upper and lower approximations while attribute values coarsen and refine are presented. Finally, the principle of coarsening or refining of the multi-granularity attribute values is analyzed.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126258938","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255140
Jia-li Feng
A new kind of Computer, called Attribute Grid Computer based on Qualitative Mapping is presented in this paper, It is shown that a series of intelligent methods, such as Production System, Artificial Neural Network, and Support Vector Machine can be fused in the framework of qualitative criterion transformation of qualitative mapping and can be implemented by attribute grid computer. And some examples of application in pattern recognition are given too.
{"title":"Attribute Grid Computer based on Qualitative Mapping and its application in pattern Recognition","authors":"Jia-li Feng","doi":"10.1109/GRC.2009.5255140","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255140","url":null,"abstract":"A new kind of Computer, called Attribute Grid Computer based on Qualitative Mapping is presented in this paper, It is shown that a series of intelligent methods, such as Production System, Artificial Neural Network, and Support Vector Machine can be fused in the framework of qualitative criterion transformation of qualitative mapping and can be implemented by attribute grid computer. And some examples of application in pattern recognition are given too.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114324629","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255030
Qingyun Yang, Chunjie Wang, Changsheng Zhang
Task Assignment Problems (TAPs) in distributed computer system are general NP-hard and usually modeled as integer programming discrete problems. Many algorithms are proposed to resolve those problems. Discrete particle swarm algorithm (DPS) is a newly developed method to solve constraint satisfaction problem (CSP) which has advantage on search capacity and can find more solutions. We proposed an improved DPS to solve TAP in this paper. DPS has a special operator namely coefficient multiplying speed, which is designed for CSP but does not exist in other discrete problems. Thus we redefined a coefficient multiplying speed operator with probability selection. We analyzed the speed and position updating formula then we derived a refined position updating formula. Several experiments are carried out to test our DPS. Experimental results show that our algorithm has more efficient search capacity, higher success rate, less running time and more robust.
{"title":"An efficient discrete particle swarm algorithm for Task Assignment Problems","authors":"Qingyun Yang, Chunjie Wang, Changsheng Zhang","doi":"10.1109/GRC.2009.5255030","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255030","url":null,"abstract":"Task Assignment Problems (TAPs) in distributed computer system are general NP-hard and usually modeled as integer programming discrete problems. Many algorithms are proposed to resolve those problems. Discrete particle swarm algorithm (DPS) is a newly developed method to solve constraint satisfaction problem (CSP) which has advantage on search capacity and can find more solutions. We proposed an improved DPS to solve TAP in this paper. DPS has a special operator namely coefficient multiplying speed, which is designed for CSP but does not exist in other discrete problems. Thus we redefined a coefficient multiplying speed operator with probability selection. We analyzed the speed and position updating formula then we derived a refined position updating formula. Several experiments are carried out to test our DPS. Experimental results show that our algorithm has more efficient search capacity, higher success rate, less running time and more robust.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122669003","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255163
Kunrong Chen, Fen Lin, Qing Tan, Zhongzhi Shi
A basic problem of intelligent systems is choosing adaptive action to perform in a non-stationary environment. Due to the combinatorial complexity of actions, agent cannot possibly consider every option available to it at every instant in time. It needs to find good policies that dictate optimum actions to perform in each situation. This paper proposes an algorithm, called UQ-learning, to better solve action selection problem by using reinforcement learning and utility function. Reinforcement learning can provide the information of environment and utility function is used to balance Exploration-Exploitation dilemma. We implement our method with maze navigation tasks in a non-stationary environment. The results of simulated experiments show that utility-based reinforcement learning approach is more effective and efficient compared with Q-learning and Recency-Based Exploration.
{"title":"Adaptive action selection using utility-based reinforcement learning","authors":"Kunrong Chen, Fen Lin, Qing Tan, Zhongzhi Shi","doi":"10.1109/GRC.2009.5255163","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255163","url":null,"abstract":"A basic problem of intelligent systems is choosing adaptive action to perform in a non-stationary environment. Due to the combinatorial complexity of actions, agent cannot possibly consider every option available to it at every instant in time. It needs to find good policies that dictate optimum actions to perform in each situation. This paper proposes an algorithm, called UQ-learning, to better solve action selection problem by using reinforcement learning and utility function. Reinforcement learning can provide the information of environment and utility function is used to balance Exploration-Exploitation dilemma. We implement our method with maze navigation tasks in a non-stationary environment. The results of simulated experiments show that utility-based reinforcement learning approach is more effective and efficient compared with Q-learning and Recency-Based Exploration.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122724932","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255035
Qing Yang, Wei Chen, Bin Wen
For expressing the fuzziness and uncertainty of domain knowledge, realizing the semantic retrieval of fuzzy information, this paper produces an extended fuzzy ontology model and proposes a kind of semantic query expansion technology which can implement semantic information query based on the property values and the relationships of fuzzy concepts. The extended fuzzy ontology provides appropriate support for Learning Evaluation. To access the effect of the proposed model, many experiments have been given for the performance evaluation. The results show that this system can improve retrieval accuracy and promote intelligent semantic query.
{"title":"Fuzzy ontology generation model using fuzzy clustering for learning evaluation","authors":"Qing Yang, Wei Chen, Bin Wen","doi":"10.1109/GRC.2009.5255035","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255035","url":null,"abstract":"For expressing the fuzziness and uncertainty of domain knowledge, realizing the semantic retrieval of fuzzy information, this paper produces an extended fuzzy ontology model and proposes a kind of semantic query expansion technology which can implement semantic information query based on the property values and the relationships of fuzzy concepts. The extended fuzzy ontology provides appropriate support for Learning Evaluation. To access the effect of the proposed model, many experiments have been given for the performance evaluation. The results show that this system can improve retrieval accuracy and promote intelligent semantic query.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126272126","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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