Solving complex problems by multi-agent systems in distributed environments requires new approximate reasoning methods based on new computing paradigms. One such recently emerging computing paradigm is Granular Comput...Solving complex problems by multi-agent systems in distributed environments requires new approximate reasoning methods based on new computing paradigms. One such recently emerging computing paradigm is Granular Computing(GC). We discuss the Rough-Granular Computing(RGC) approach to modeling of computations in complex adaptive systems and multiagent systems as well as for approximate reasoning about the behavior of such systems. The RGC methods have been successfully applied for solving complex problems in areas such as identification of objects or behavioral patterns by autonomous systems, web mining, and sensor fusion.展开更多
The rapid expansion of the Internet has resulted not only in the ever growing amount of data therein stored,but also in the burgeoning complexity of the concepts and phenomena pertaining to those data.This issue has b...The rapid expansion of the Internet has resulted not only in the ever growing amount of data therein stored,but also in the burgeoning complexity of the concepts and phenomena pertaining to those data.This issue has been vividly compared by the renowned statistician,prof.Friedman of Stanford University,to the advances in human mobility from the period of walking afoot to the era of jet travel.These essential changes in data have brought new challenges to the development of new data mining methods,especially that the treatment of these data increasingly involves complex processes that elude classic modeling paradigms."Hot" datasets like biomedical,financial or net user behavior data are just a few examples.Mining such temporal or stream data is on the agenda of many research centers and companies worldwide.In the data mining community,there is a rapidly growing interest in developing methods for process mining,e.g.,for discovery of structures of temporal processes from data.Works on process mining have recently been undertaken by many renowned centers worldwide.This research is also related to functional data analysis,cognitive networks,and dynamical system modeling,e.g.,in biology.In the lecture,we outline an approach to discovery of processes from data and domain knowledge which is based on the rough-granular computing.展开更多
粒球邻域粗糙集(Granular Ball Neighborhood Rough Set,GBNRS)作为一种经典的属性约简方法,要求粒球的纯度严格为1,在类边界处会产生大量样本数为1的粒球.这些粒球通常被误判为离群点并剔除,导致类边界信息的丢失.为了解决此问题.文中...粒球邻域粗糙集(Granular Ball Neighborhood Rough Set,GBNRS)作为一种经典的属性约简方法,要求粒球的纯度严格为1,在类边界处会产生大量样本数为1的粒球.这些粒球通常被误判为离群点并剔除,导致类边界信息的丢失.为了解决此问题.文中首先定义模糊纯度函数,融合隶属度与类别标签,作为粒球质量的评价指标.此函数基于动态质量评估和优化策略,综合考虑数据点的隶属度、数据点的类标签及粒球的类标签三重信息.然后,在粒球分裂过程中,引入分类显著性阈值β,自适应调整M-means的m值,构建模糊纯度粒球生成算法.进一步地,针对粗糙集属性约简问题,设计前向属性约简算法,并提出基于模糊纯度粒球的粗糙集模型(Rough Set Model Based on Fuzzy Purity Granular Ball,FPGBRS).最后,在12个真实数据集上的实验表明,FPGBRS可提升分类精度和效率.展开更多
基金The grant3 T11C 00226 from Min istroyf ScientifiRcesearchand InformationTechnologyoftheRepublicofPoland.
文摘Solving complex problems by multi-agent systems in distributed environments requires new approximate reasoning methods based on new computing paradigms. One such recently emerging computing paradigm is Granular Computing(GC). We discuss the Rough-Granular Computing(RGC) approach to modeling of computations in complex adaptive systems and multiagent systems as well as for approximate reasoning about the behavior of such systems. The RGC methods have been successfully applied for solving complex problems in areas such as identification of objects or behavioral patterns by autonomous systems, web mining, and sensor fusion.
基金supported by the grant N N516 368334 from Ministry of Science and Higher Education of the Republic of Poland and by the grant Innovative Economy Operational Programme 2007-2013(Priority Axis 1.Research and development of new technologies)managed by Ministry of Regional Development of the Republic of Poland.
文摘The rapid expansion of the Internet has resulted not only in the ever growing amount of data therein stored,but also in the burgeoning complexity of the concepts and phenomena pertaining to those data.This issue has been vividly compared by the renowned statistician,prof.Friedman of Stanford University,to the advances in human mobility from the period of walking afoot to the era of jet travel.These essential changes in data have brought new challenges to the development of new data mining methods,especially that the treatment of these data increasingly involves complex processes that elude classic modeling paradigms."Hot" datasets like biomedical,financial or net user behavior data are just a few examples.Mining such temporal or stream data is on the agenda of many research centers and companies worldwide.In the data mining community,there is a rapidly growing interest in developing methods for process mining,e.g.,for discovery of structures of temporal processes from data.Works on process mining have recently been undertaken by many renowned centers worldwide.This research is also related to functional data analysis,cognitive networks,and dynamical system modeling,e.g.,in biology.In the lecture,we outline an approach to discovery of processes from data and domain knowledge which is based on the rough-granular computing.
文摘粒球邻域粗糙集(Granular Ball Neighborhood Rough Set,GBNRS)作为一种经典的属性约简方法,要求粒球的纯度严格为1,在类边界处会产生大量样本数为1的粒球.这些粒球通常被误判为离群点并剔除,导致类边界信息的丢失.为了解决此问题.文中首先定义模糊纯度函数,融合隶属度与类别标签,作为粒球质量的评价指标.此函数基于动态质量评估和优化策略,综合考虑数据点的隶属度、数据点的类标签及粒球的类标签三重信息.然后,在粒球分裂过程中,引入分类显著性阈值β,自适应调整M-means的m值,构建模糊纯度粒球生成算法.进一步地,针对粗糙集属性约简问题,设计前向属性约简算法,并提出基于模糊纯度粒球的粗糙集模型(Rough Set Model Based on Fuzzy Purity Granular Ball,FPGBRS).最后,在12个真实数据集上的实验表明,FPGBRS可提升分类精度和效率.
