Since the introduction of rough sets in 1982 by Professor Zdzisaw Pawlak, we have witnessed great advances in both theory and applications. Rough set theory is closely related to knowledge technology in a variety of...Since the introduction of rough sets in 1982 by Professor Zdzisaw Pawlak, we have witnessed great advances in both theory and applications. Rough set theory is closely related to knowledge technology in a variety of forms such as knowledge discovery, approximate reasoning, intelligent and multiagent system design, knowledge intensive computations. The cutting-edge knowledge technologies have great impact on learning, pattern recognition, machine intelligence and automation of acquisition, transformation, communication, exploration and exploitation of knowledge. A principal thrust of such technologies is the utilization of methodologies that facilitate knowledge processing. To present the state-of-the-art scientific results, encourage academic and industrial interaction, and promote collaborative research in rough sets and knowledge technology worldwide, the 3rd International Conference on Rough Sets and Knowledge Technology will be held in Chengdu, China, May 17～19, 2008. It will provide a forum for researchers to discuss new results and exchange ideas, following the successful RSKT'06 (Chongqing, China) and JRS'07 (RSKT'07 together with RSFDGrC'07) (Toronto, Canada).展开更多
Submission Deadline： 10 December 2010Since the introduction of rough sets in 1982 by Professor Zdzislaw Pawlak, we have witnessed great advances in both theory and applications. In order to promote development of rou...Submission Deadline： 10 December 2010Since the introduction of rough sets in 1982 by Professor Zdzislaw Pawlak, we have witnessed great advances in both theory and applications. In order to promote development of rough sets, we are preparing a special issue on ＂Artificial Intelligence with Rough Sets＂ published by JEST （International）, Journal of Electronic Science and Technology, which is a refereed international journal focusing on IT area. The aim of this special issue is to present the current state of the research in this area, oriented towards both theoretical and applications aspects of rough sets.展开更多
This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory （RST）. The proposed algorithm is unified in its approach to clusterin...This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory （RST）. The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease. The results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency.展开更多
In this paper,we propose a novel Intrusion Detection System (IDS) architecture utilizing both the evidence theory and Rough Set Theory (RST). Evidence theory is an effective tool in dealing with uncertainty question. ...In this paper,we propose a novel Intrusion Detection System (IDS) architecture utilizing both the evidence theory and Rough Set Theory (RST). Evidence theory is an effective tool in dealing with uncertainty question. It relies on the expert knowledge to provide evidences,needing the evidences to be independent,and this make it difficult in application. To solve this problem,a hybrid system of rough sets and evidence theory is proposed. Firstly,simplification are made based on Variable Precision Rough Set (VPRS) conditional entropy. Thus,the Basic Belief Assignment (BBA) for all evidences can be calculated. Secondly,Dempster’s rule of combination is used,and a decision-making is given. In the proposed approach,the difficulties in acquiring the BBAs are solved,the correlativity among the evidences is reduced and the subjectivity of evidences is weakened. An illustrative example in an intrusion detection shows that the two theories combination is feasible and effective.展开更多
Analytic Hierarchy Process (AHP) method can be used to solve the tasks of multi-criterion decision system fields, but some complicated questions processed by AHP cannot be easily solved by means of the general method....Analytic Hierarchy Process (AHP) method can be used to solve the tasks of multi-criterion decision system fields, but some complicated questions processed by AHP cannot be easily solved by means of the general method. It is because of being unsatisfied with consistency condition or judgment matrix too intricate to solve, which causes AHP invalidation. So in order to resolve this problem, AHP knowledge systems reduced with the aid of Genetic Algorithms (GA) were proposed, which directly acquired the order of AHP issue through the rule of Rough Sets Theory (RST) method, or solved the tasks reduced by RST with classical AHP method. On this condition, the compare decision system of region informatization level was solved, and the results solved were the same to those by classical AHP, which denoted that this method was more simple and reliable, besides the four rules of changing AHP system into RST Decision System.展开更多
The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attr...The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.展开更多
高效准确的短期电力负荷预测对提升新型电力系统经济运行十分重要。针对极端天气场景下负荷预测数据量较少、随机性较强的特点,提出一种基于张量低秩补全算法的短期负荷预测模型,并选取极端高温场景展开研究。首先,给出极端天气定义,并...高效准确的短期电力负荷预测对提升新型电力系统经济运行十分重要。针对极端天气场景下负荷预测数据量较少、随机性较强的特点,提出一种基于张量低秩补全算法的短期负荷预测模型,并选取极端高温场景展开研究。首先,给出极端天气定义,并基于改进型炎热指数和气温两项指标进行数据筛选;其次,提出一种基于张量的负荷数据补全模型,补全缺失数据;然后,通过Pearson相关性分析筛选输入特征量,构建基于长短时记忆(long short term memory, LSTM)网络和粗糙集理论(rough set theory, RST)的LSTM-RST短期负荷预测模型;最后,以苏州某地实际负荷数据设置算例进行验证,仿真结果表明,所提短期负荷预测方法具有较高的准确性。展开更多
文摘Since the introduction of rough sets in 1982 by Professor Zdzisaw Pawlak, we have witnessed great advances in both theory and applications. Rough set theory is closely related to knowledge technology in a variety of forms such as knowledge discovery, approximate reasoning, intelligent and multiagent system design, knowledge intensive computations. The cutting-edge knowledge technologies have great impact on learning, pattern recognition, machine intelligence and automation of acquisition, transformation, communication, exploration and exploitation of knowledge. A principal thrust of such technologies is the utilization of methodologies that facilitate knowledge processing. To present the state-of-the-art scientific results, encourage academic and industrial interaction, and promote collaborative research in rough sets and knowledge technology worldwide, the 3rd International Conference on Rough Sets and Knowledge Technology will be held in Chengdu, China, May 17～19, 2008. It will provide a forum for researchers to discuss new results and exchange ideas, following the successful RSKT'06 (Chongqing, China) and JRS'07 (RSKT'07 together with RSFDGrC'07) (Toronto, Canada).
文摘Submission Deadline： 10 December 2010Since the introduction of rough sets in 1982 by Professor Zdzislaw Pawlak, we have witnessed great advances in both theory and applications. In order to promote development of rough sets, we are preparing a special issue on ＂Artificial Intelligence with Rough Sets＂ published by JEST （International）, Journal of Electronic Science and Technology, which is a refereed international journal focusing on IT area. The aim of this special issue is to present the current state of the research in this area, oriented towards both theoretical and applications aspects of rough sets.
文摘This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory （RST）. The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease. The results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency.
基金Supported by the National Natural Science Foundation of China (No. 60774029)
文摘In this paper,we propose a novel Intrusion Detection System (IDS) architecture utilizing both the evidence theory and Rough Set Theory (RST). Evidence theory is an effective tool in dealing with uncertainty question. It relies on the expert knowledge to provide evidences,needing the evidences to be independent,and this make it difficult in application. To solve this problem,a hybrid system of rough sets and evidence theory is proposed. Firstly,simplification are made based on Variable Precision Rough Set (VPRS) conditional entropy. Thus,the Basic Belief Assignment (BBA) for all evidences can be calculated. Secondly,Dempster’s rule of combination is used,and a decision-making is given. In the proposed approach,the difficulties in acquiring the BBAs are solved,the correlativity among the evidences is reduced and the subjectivity of evidences is weakened. An illustrative example in an intrusion detection shows that the two theories combination is feasible and effective.
基金Sponsored by the National Natural Science Foundation of China(Grant No70472075)the Project of the Jiangxi Province Natural Science Foundation(Grant No2007GZS0898)the Project of Science and Technology for the Department of Education of Jiangxi Province (Grant No2007-183)
文摘Analytic Hierarchy Process (AHP) method can be used to solve the tasks of multi-criterion decision system fields, but some complicated questions processed by AHP cannot be easily solved by means of the general method. It is because of being unsatisfied with consistency condition or judgment matrix too intricate to solve, which causes AHP invalidation. So in order to resolve this problem, AHP knowledge systems reduced with the aid of Genetic Algorithms (GA) were proposed, which directly acquired the order of AHP issue through the rule of Rough Sets Theory (RST) method, or solved the tasks reduced by RST with classical AHP method. On this condition, the compare decision system of region informatization level was solved, and the results solved were the same to those by classical AHP, which denoted that this method was more simple and reliable, besides the four rules of changing AHP system into RST Decision System.
基金Anhui Province Natural Science Research Project of Colleges and Universities(2023AH040321)Excellent Scientific Research and Innovation Team of Anhui Colleges(2022AH010098).
文摘The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.
文摘高效准确的短期电力负荷预测对提升新型电力系统经济运行十分重要。针对极端天气场景下负荷预测数据量较少、随机性较强的特点,提出一种基于张量低秩补全算法的短期负荷预测模型,并选取极端高温场景展开研究。首先,给出极端天气定义,并基于改进型炎热指数和气温两项指标进行数据筛选;其次,提出一种基于张量的负荷数据补全模型,补全缺失数据;然后,通过Pearson相关性分析筛选输入特征量,构建基于长短时记忆(long short term memory, LSTM)网络和粗糙集理论(rough set theory, RST)的LSTM-RST短期负荷预测模型;最后,以苏州某地实际负荷数据设置算例进行验证,仿真结果表明,所提短期负荷预测方法具有较高的准确性。
