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属性频率
相关语句
  attribute frequency
     Algorithm of Attribute Reduction Based on Attribute Frequency Function in Rough Set
     基于属性频率函数的粗糙集属性约简算法
短句来源
     An algorithm of attribute frequency reduction based on discernibility matrix
     基于可辨识矩阵的属性频率约简算法
短句来源
     Firstly, the concept of association matrix is proposed to improve efficiency. By using the information of attribute frequency, a new algorithm based on dependence of decision attribute on condition attribute is proposed.
     1.为提高属性约简的效率,基于对区分矩阵的研究提出了关联矩阵的概念,同时,依照决策属性对条件属性的依赖度,利用关联阵中属性频率的信息,提出了一种基于关联矩阵的属性约简算法。
短句来源
     The item of attribute frequency in the scan vector was taken as heuristic information to improve the efficiency of attributes reduction.
     以扫描向量中的属性频率项作为属性约简搜索的启发信息,提高了属性约简效率.
短句来源
     On the basis of research of two previous attributes reduction arithmetics,we proposed an attributes reduction algorithm based on the attribute frequency function in rough set. The algorithm promised that each computation can get a reduction,and it is of lower time complexity than Jelonek's algorithm.
     以现有的两个粗糙集属性约简算法为基础,提出了一种基于属性频率函数的粗糙集(Rough Set)属性约简算法,该算法既可保证每次计算都能得到一个约简,又具有较好的时间复杂性.
短句来源
  “属性频率”译为未确定词的双语例句
     Based on the concept of discernibility matrix,a new concept called association matrix is proposed in this paper,and an attribute reduction algorithm is given accordingly.
     为改进差别矩阵的应用,提高约简效率,文章提出了关联矩阵的概念,同时,依照决策属性对条件属性的依赖程度,利用关联阵中属性频率的信息,提出了一种属性约简算法。
短句来源
     Attribute Reduction Based on Indiscernibility Matrix
     基于不可区分矩阵的属性频率约简
短句来源
     Through the study in the data reduction, this paper presents a new reduction algorithm named HORAFA-SVDM and an incremental algorithm of the heuristic optimal reduct finding algorithm of the frequencies function named HORAFA桰A.
     本文通过在数据约简方面的研究,提出了对利用属性频率函数的启发式约简算法的改进算法——HORAFA—SVDM算法,以及这个算法的增量版本——HORAFA—IA算法。
短句来源
     In the field of discretization, frequencies function is imported to the NS algorithmand the new reduct finding algorithm are shown. A new concept ------Candidate Coreand a new algorithm based on it are presented to solve the question in the NS algorithm with the heuristic reduct finding algorithm.
     在离散化方面,将属性频率函数引入到NS算法中,提出了相应的约简算法,同时为了解决启发式约简算法在NS算法中存在的问题提出了新的概念——候选核和基于候选核的BCC算法。
短句来源
     Then, an attribute reduction algorithm based on indiscernibility matrix is introduced.
     给出了基于不可区分矩阵的属性频率约简算法。
短句来源
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  相似匹配句对
     Attribute Reduction Based on Indiscernibility Matrix
     基于不可区分矩阵的属性频率约简
短句来源
     An algorithm of attribute frequency reduction based on discernibility matrix
     基于可辨识矩阵的属性频率约简算法
短句来源
     ON FREQUENCIES
     频率
短句来源
     X—Band Microstrip Low Noise Frequency MuItiplier
     频率指示器
短句来源
     attribute set;
     属性集;
短句来源
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Therearetwoproblemsinthetraditionaldiscretization algorithm when heuristic reduction algorithms are used to find the reduction. One is that the reduction discretizing all attributes may not be found. The other is that the heuristic reduction algorithm needs a great deal of time to get the reduction in the data sets without core. To solve the two problems , a new concept called candidate core is given, which is built on the rough set theory and attribute frequency function, and a new heuristic reduction algorithm...

Therearetwoproblemsinthetraditionaldiscretization algorithm when heuristic reduction algorithms are used to find the reduction. One is that the reduction discretizing all attributes may not be found. The other is that the heuristic reduction algorithm needs a great deal of time to get the reduction in the data sets without core. To solve the two problems , a new concept called candidate core is given, which is built on the rough set theory and attribute frequency function, and a new heuristic reduction algorithm based on candidate core (named BCC) is presented. This new heuristic reduction algorithm of BCC can find the reduction of the data sets, which discretizes all attributes. The results of experiments show that the new algorithm can improve the performance of discretization for large data sets.

针对在 Nguyen和 Skowron的离散化算法中进行启发式约简时会出现某些属性不能进行离散化问题 ,以及在无核数据集中启发式约简算法计算量比较大等问题 ,在粗糙集理论和属性频率函数的基础上给出一个新概念——候选核 ,并提出一种新的用于连续值属性离散化的约简算法——基于候选核的启发式约简算法 (简称 BCC)。该算法可以寻找到能对所有属性进行离散化的约简。实验表明 ,所提出的 BCC算法能提高大数据集的离散化效果。

According to the indiscernibility relation, in this paper, the concept of indiscernibility matrix is proposed and the relation between discernibility matrix and indiscernibility matrix is shown. The advantage of indiscernibility matrix is pointed out. Then, an attribute reduction algorithm based on indiscernibility matrix is introduced. Compared with discernibility matrix algorithm, this algorithm greatly reduces running time and memory space.

根据不可区分关系,提出了不可区分矩阵的概念。证明了不可区分矩阵与区分矩阵的关系,指出了不可区分矩阵约简算法的优势。给出了基于不可区分矩阵的属性频率约简算法。相对于区分矩阵算法,该算法在时间和存储空间花费上都有较大的改善和提高。

Heuristic knowledge reduction approach based on discernibility matrix and strong compressible set is proposed. Knowledge is expressed first using discernibility matrix during the decision table's relative reduction process and attributes are selected according to elements' length in the discernibility matrix and frequency attribute. Both of attribute weight frequency and strong compressible set are used to simplify discernibility matrix so that computing complexity is decreased and reduction efficiency is...

Heuristic knowledge reduction approach based on discernibility matrix and strong compressible set is proposed. Knowledge is expressed first using discernibility matrix during the decision table's relative reduction process and attributes are selected according to elements' length in the discernibility matrix and frequency attribute. Both of attribute weight frequency and strong compressible set are used to simplify discernibility matrix so that computing complexity is decreased and reduction efficiency is increased. It has been proved that the problem of searching minimum relative reduction is a NP hard problem. The minimum reduction can be obtained using proposed method in most cases. Otherwise, there must be a feasible solution. The practical results show that the approach is quick and effective in solving relative reduction problem.

提出了基于区分矩阵与强等价集的启发式知识约简方法。在决策表的相对约简过程中采用区分矩阵来表达知识,并利用区分矩阵中项的长度和每个属性的频率作为启发信息进行属性的选择。同时利用属性加权频率和强等价集概念化简区分矩阵,既减小了计算复杂度又提高了约简效率。现已证明,寻找决策表中最小相对约简问题是典型的问题,采用该算法在大多数情况下能够找到最小约简,即使在未找到最小约简的情况下,也能找到次优解。通过实例分析,证明该算法是求解属性相对约简的快速、有效的方法.

 
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