| 1. | Improved decision matrix and attribute reduction
改进的决策矩阵及其属性约简方法 |
| 2. | Firstly , concepts on the interval number , interval numbers decision matrix of experts and a method of nomalizing the decision matrix are introduced
首先，给出了有关区间数、专家群体判断区间数决策矩阵的定义及其决策矩阵的规范化方法。 |
| 3. | The method based on expected values is given for fuzzy multipleattribute decision making problems , in which the information on attribute weights is completely unknown or partly known
定义了期望值决策矩阵的概念，对于权重信息完全未知或只有部分权重信息的情形，给出了一种基于期望值的模糊多属性决策方法。 |
| 4. | Methods by defining the concept of expected value decision matrix ( based on the ideal solution or the negative - ideal solution ) , using the preference information on alternatives , the best alternative can be gated
方法通过引入基于负理想点下和基于正理想点下的期望值决策矩阵概念，结合决策者的偏好信息，对方案进行排序。 |
| 5. | It not only could solve the problem of learning on the incremental data sets , but also could considerably reduce the size of traditional decision matrix and avoid the repeated computation in traditional decision matrix algorithm
这不仅解决了动态数据环境下归纳学习问题，而且能降低矩阵空间规模，避免了传统决策矩阵算法中的重复计算。 |
| 6. | Then , based on defining opposite deviation between each element of each s interval numbers decision matrix and corresponding elements of expert ' s interval numbers decision matrix , an analytic method to each s assessment level is given
通过定义专家给出的决策矩阵中各个元素与综合评价矩阵中对应元素之间的相对偏差，给出了基于区间数决策矩阵形式偏好信息的专家评判水平的分析方法。 |
| 7. | In this paper , sensitivity analysis for multiple attribute decision making is studied , the perturbation upper bounds of each attribute value and attribute weight of the alternative , which make the alternative remain the original position , are obtained
多属性决策的灵敏度分析能有效地揭示决策矩阵及属性权重关于决策方案的内在变化规律，对此我们基于多属性决策的灵敏度分析的现状展开了更深入的讨论。 |
| 8. | The main ideal is to divide the decision system into several subsystems based on the decision classes and construct the new decision matrix , and then to transform the inductive learning for the decision system into the incremental learning based on the new decision matrix
其主要思想是基于决策类将决策系统分割为多个子系统，并构造其新的决策矩阵，然后将决策系统上的归纳学习转化为新决策矩阵下的递增式学习。 |
| 9. | After a brief description of concepts involved in the paper , such as rough set theory and the indiscernibility relation , the methods of how to preprocess the loss value of data and how to discrete the data are discussed . the algorithms of attribute < wp = 6 > reduction and value reduction in rough set theory are researched in depth , and then the ordinary reduction algorithms are given . the improved algorithms based on discernibility matrix and decision matrix are put forward
论文在对所涉及的粗糙集理论以及不可分辨关系等概念进行了简要的阐述后，接着对数据缺失值如何进行预处理以及如何进行数据的离散化处理等方法进行了探讨，然后展开了对粗糙集理论中属性约简以及值约简等算法更进一步的研究，给出了常规约简算法，并在此基础上提出了基于可辨识矩阵以及决策矩阵的改进算法，结果表明算法具有更高的约简效果。 |
| 10. | The extended discernibility matrices and extended decision matrices have been introduced , new attribute reduction algorithm and incremental updating algorithm ( namely , attribute reduction algorithm based on extended discernibility matrix and incremental rule acquisition algorithm based on extended decision matrix ) have been presented , and incremental updating algorithm of mica has been discussed and researched
摘要引入扩展差别矩阵和扩展决策矩阵，提出了新的属性约简算法和增量更新算法，即基于扩展差别矩阵的属性约简算法和基于扩展决策矩阵的增量式规则提取算法，讨论了规则的增量更新算法。 |
