The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that th...The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.展开更多
Community detection in networks has been studied extensively in the last decade. Many criteria, expressing the quality of the partitions obtained, as well as a few exact algorithms and a large number of heuristics hav...Community detection in networks has been studied extensively in the last decade. Many criteria, expressing the quality of the partitions obtained, as well as a few exact algorithms and a large number of heuristics have been proposed. The parsimony criterion consists in minimizing the number of edges added or removed from the given network in order to transform it into a set of disjoint cliques.Recently Zhang, Qiu and Zhang have proposed a weighted parsimony model in which a weight coefficient is introduced to balance the numbers of inserted and deleted edges. These authors propose rules to select a good value of the coefficient, use simulated annealing to find optimal or near-optimal solutions and solve a series of real and artificial instances. In the present paper, an algorithm is proposed for solving exactly the weighted parsimony problem for all values of the parameter. This algorithm is based on iteratively solving the problem for a set of given values of the parameter using a row generation algorithm. This procedure is combined with a search procedure to find all lowest breakpoints of the value curve(i.e., the weighted sum of inserted and deleted edges). Computational results on a series of artificial and real world networks from the literature are reported. It appears that several partitions for the same network may be informative and that the set of solutions usually contains at least one intuitively appealing partition.展开更多
基金The authors would like to acknowledge the funding support of the National Natural Science Foundation of China （50579009, 70471090） the National 10 th Five Year Scientific Project of China for Tackling the Key Problems （2004BA608B-02 - 02） and the Excellence Youth Teacher Sustentation Fund Program of the Ministry of Education of China （Department of Education and Personnel [2002] 350）.
文摘The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.
文摘Community detection in networks has been studied extensively in the last decade. Many criteria, expressing the quality of the partitions obtained, as well as a few exact algorithms and a large number of heuristics have been proposed. The parsimony criterion consists in minimizing the number of edges added or removed from the given network in order to transform it into a set of disjoint cliques.Recently Zhang, Qiu and Zhang have proposed a weighted parsimony model in which a weight coefficient is introduced to balance the numbers of inserted and deleted edges. These authors propose rules to select a good value of the coefficient, use simulated annealing to find optimal or near-optimal solutions and solve a series of real and artificial instances. In the present paper, an algorithm is proposed for solving exactly the weighted parsimony problem for all values of the parameter. This algorithm is based on iteratively solving the problem for a set of given values of the parameter using a row generation algorithm. This procedure is combined with a search procedure to find all lowest breakpoints of the value curve(i.e., the weighted sum of inserted and deleted edges). Computational results on a series of artificial and real world networks from the literature are reported. It appears that several partitions for the same network may be informative and that the set of solutions usually contains at least one intuitively appealing partition.
