The classical rough set can not show the fuzziness and the importance of objects in decision procedure because it uses definite form to express each object. In order to solve this problem,this paper firstly introduces...The classical rough set can not show the fuzziness and the importance of objects in decision procedure because it uses definite form to express each object. In order to solve this problem,this paper firstly introduces a special decision table in which each object has a membership degree to show its fuzziness and has been assigned a weight to show its importance in decision procedure. Then,the special decision table is studied and the relevant rough set model is provided. In the meantime,relevant definitions and theorems are proposed. On the above basis,an attribute reduction algorithm is presented. Finally,feasibility of the relevant rough set model and the presented attribute reduction algorithm are verified by an example.展开更多
In many practical situation, some of the attribute values for an object may be interval and set-valued. This paper introduces the interval and set-valued information systems and decision systems. According to the sema...In many practical situation, some of the attribute values for an object may be interval and set-valued. This paper introduces the interval and set-valued information systems and decision systems. According to the semantic relation of attribute values, interval and set-valued information systems can be classified into two categories: disjunctive (Type 1) and conjunctive (Type 2) systems. In this paper, we mainly focus on semantic interpretation of Type 1. Then, we define a new fuzzy preference relation and construct a fuzzy rough set model for interval and set-valued information systems. Moreover, based on the new fuzzy preference relation, the concepts of the significance measure of condition attributes and the relative significance measure of condition attributes are given in interval and set-valued decision information systems by the introduction of fuzzy positive region and the dependency degree. And on this basis, a heuristic algorithm for calculating fuzzy positive region reduction in interval and set-valued decision information systems is given. Finally, we give an illustrative example to substantiate the theoretical arguments. The results will help us to gain much more insights into the meaning of fuzzy rough set theory. Furthermore, it has provided a new perspective to study the attribute reduction problem in decision systems.展开更多
In this paper, we study the problem of rule extraction from data sets using the rough set method. For inconsistent rules due to improper selection of split-points during discretization, and/or to lack of information, ...In this paper, we study the problem of rule extraction from data sets using the rough set method. For inconsistent rules due to improper selection of split-points during discretization, and/or to lack of information, we propose two methods to remove their inconsistency based on irregular decision tables. By using these methods, inconsistent rules are eliminated as far as possible, without affecting the remaining consistent rules. Experimental test indicates that use of the new method leads to an improvement in the mean accuracy of the extracted rules.展开更多
铁路工程是典型的带状结构,具有线长、点广等特点,为实现铁路工程全线全方位、多角度、多尺度的可视化展示及综合分析,融合建筑信息模型(BIM,Building Information Modeling)、地理信息系统(GIS,Geographic Information Systems)、数字...铁路工程是典型的带状结构,具有线长、点广等特点,为实现铁路工程全线全方位、多角度、多尺度的可视化展示及综合分析,融合建筑信息模型(BIM,Building Information Modeling)、地理信息系统(GIS,Geographic Information Systems)、数字高程、倾斜摄影及矢量地图等数据,设计并研发铁路工程电子沙盘系统。该系统通过对实体铁路工程仿真呈现,将施工过程信息动态附加,形成可交付运营维护的建设数字资产。在京张(北京—张家口)、京雄(北京—雄安)及其他重大铁路项目中的应用结果表明,电子沙盘系统具有较好的实用性、可扩展性及可视化效果,可为铁路工程各类资源高效利用、仿真分析及科学管理提供服务,也可为其他铁路项目开展电子沙盘的设计与开发提供参考。展开更多
Attribute reduction,also known as feature selection,for decision information systems is one of the most pivotal issues in machine learning and data mining.Approaches based on the rough set theory and some extensions w...Attribute reduction,also known as feature selection,for decision information systems is one of the most pivotal issues in machine learning and data mining.Approaches based on the rough set theory and some extensions were proved to be efficient for dealing with the problemof attribute reduction.Unfortunately,the intuitionistic fuzzy sets based methods have not received much interest,while these methods are well-known as a very powerful approach to noisy decision tables,i.e.,data tables with the low initial classification accuracy.Therefore,this paper provides a novel incremental attribute reductionmethod to dealmore effectivelywith noisy decision tables,especially for highdimensional ones.In particular,we define a new reduct and then design an original attribute reduction method based on the distance measure between two intuitionistic fuzzy partitions.It should be noted that the intuitionistic fuzzypartitiondistance iswell-knownas aneffectivemeasure todetermine important attributes.More interestingly,an incremental formula is also developed to quickly compute the intuitionistic fuzzy partition distance in case when the decision table increases in the number of objects.This formula is then applied to construct an incremental attribute reduction algorithm for handling such dynamic tables.Besides,some experiments are conducted on real datasets to show that our method is far superior to the fuzzy rough set based methods in terms of the size of reduct and the classification accuracy.展开更多
基金supported by the Foundation and Frontier Technologies Research Plan Projects of Henan Province of China under Grant No. 102300410266
文摘The classical rough set can not show the fuzziness and the importance of objects in decision procedure because it uses definite form to express each object. In order to solve this problem,this paper firstly introduces a special decision table in which each object has a membership degree to show its fuzziness and has been assigned a weight to show its importance in decision procedure. Then,the special decision table is studied and the relevant rough set model is provided. In the meantime,relevant definitions and theorems are proposed. On the above basis,an attribute reduction algorithm is presented. Finally,feasibility of the relevant rough set model and the presented attribute reduction algorithm are verified by an example.
文摘In many practical situation, some of the attribute values for an object may be interval and set-valued. This paper introduces the interval and set-valued information systems and decision systems. According to the semantic relation of attribute values, interval and set-valued information systems can be classified into two categories: disjunctive (Type 1) and conjunctive (Type 2) systems. In this paper, we mainly focus on semantic interpretation of Type 1. Then, we define a new fuzzy preference relation and construct a fuzzy rough set model for interval and set-valued information systems. Moreover, based on the new fuzzy preference relation, the concepts of the significance measure of condition attributes and the relative significance measure of condition attributes are given in interval and set-valued decision information systems by the introduction of fuzzy positive region and the dependency degree. And on this basis, a heuristic algorithm for calculating fuzzy positive region reduction in interval and set-valued decision information systems is given. Finally, we give an illustrative example to substantiate the theoretical arguments. The results will help us to gain much more insights into the meaning of fuzzy rough set theory. Furthermore, it has provided a new perspective to study the attribute reduction problem in decision systems.
基金the Basic Research Foundation of Tsinghua University (No. JC2001029) and the National High-Tech Research and Development Program of China (No. 863-511-930-004)
文摘In this paper, we study the problem of rule extraction from data sets using the rough set method. For inconsistent rules due to improper selection of split-points during discretization, and/or to lack of information, we propose two methods to remove their inconsistency based on irregular decision tables. By using these methods, inconsistent rules are eliminated as far as possible, without affecting the remaining consistent rules. Experimental test indicates that use of the new method leads to an improvement in the mean accuracy of the extracted rules.
文摘铁路工程是典型的带状结构,具有线长、点广等特点,为实现铁路工程全线全方位、多角度、多尺度的可视化展示及综合分析,融合建筑信息模型(BIM,Building Information Modeling)、地理信息系统(GIS,Geographic Information Systems)、数字高程、倾斜摄影及矢量地图等数据,设计并研发铁路工程电子沙盘系统。该系统通过对实体铁路工程仿真呈现,将施工过程信息动态附加,形成可交付运营维护的建设数字资产。在京张(北京—张家口)、京雄(北京—雄安)及其他重大铁路项目中的应用结果表明,电子沙盘系统具有较好的实用性、可扩展性及可视化效果,可为铁路工程各类资源高效利用、仿真分析及科学管理提供服务,也可为其他铁路项目开展电子沙盘的设计与开发提供参考。
基金funded by Hanoi University of Industry under Grant Number 27-2022-RD/HD-DHCN (URL:https://www.haui.edu.vn/).
文摘Attribute reduction,also known as feature selection,for decision information systems is one of the most pivotal issues in machine learning and data mining.Approaches based on the rough set theory and some extensions were proved to be efficient for dealing with the problemof attribute reduction.Unfortunately,the intuitionistic fuzzy sets based methods have not received much interest,while these methods are well-known as a very powerful approach to noisy decision tables,i.e.,data tables with the low initial classification accuracy.Therefore,this paper provides a novel incremental attribute reductionmethod to dealmore effectivelywith noisy decision tables,especially for highdimensional ones.In particular,we define a new reduct and then design an original attribute reduction method based on the distance measure between two intuitionistic fuzzy partitions.It should be noted that the intuitionistic fuzzypartitiondistance iswell-knownas aneffectivemeasure todetermine important attributes.More interestingly,an incremental formula is also developed to quickly compute the intuitionistic fuzzy partition distance in case when the decision table increases in the number of objects.This formula is then applied to construct an incremental attribute reduction algorithm for handling such dynamic tables.Besides,some experiments are conducted on real datasets to show that our method is far superior to the fuzzy rough set based methods in terms of the size of reduct and the classification accuracy.
