Decision trees induction algorithms have been used for classification in a wide range of application domains. In the process of constructing a tree, the criteria of selecting test attributes will influence the classif...Decision trees induction algorithms have been used for classification in a wide range of application domains. In the process of constructing a tree, the criteria of selecting test attributes will influence the classification accuracy of the tree.In this paper,the degree of dependency of decision attribute to condition attribute,based on rough set theory,is used as a heuristic for selecting the attribute that will best separate the samples into individual classes.The result of an example shows that compared with the entropy-based approach,our approach is a better way to select nodes for constructing decision trees.展开更多
To improve the performance of the multiple classifier system, a new method of feature-decision level fusion is proposed based on knowledge discovery. In the new method, the base classifiers operate on different featur...To improve the performance of the multiple classifier system, a new method of feature-decision level fusion is proposed based on knowledge discovery. In the new method, the base classifiers operate on different feature spaces and their types depend on different measures of between-class separability. The uncertainty measures corresponding to each output of each base classifier are induced from the established decision tables （DTs） in the form of mass function in the Dempster-Shafer theory （DST）. Furthermore, an effective fusion framework is built at the feature-decision level on the basis of a generalized rough set model and the DST. The experiment for the classification of hyperspectral remote sensing images shows that the performance of the classification can be improved by the proposed method compared with that of plurality voting （PV）.展开更多
In this paper,we present a proposed method for generating a soft rough approximation as a modification and generalization of Zhaowen et al.approach.Comparisons were obtained between our approach and the previous study...In this paper,we present a proposed method for generating a soft rough approximation as a modification and generalization of Zhaowen et al.approach.Comparisons were obtained between our approach and the previous study and also.Eventually,an application on Coronavirus(COVID-19)has been presented,illustrated using our proposed concept,and some influencing results for symptoms of Coronavirus patients have been deduced.Moreover,following these concepts,we construct an algorithm and apply it to a decision-making problem to demonstrate the applicability of our proposed approach.Finally,a proposed approach that competes with others has been obtained,as well as realistic results for patients with Coronavirus.Moreover,we used MATLAB programming to obtain the results;these results are consistent with those of theWorld Health Organization and an accurate proposal competing with the method of Zhaowen et al.has been studied.Therefore,it is recommended that our proposed concept be used in future decision making.展开更多
In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result...In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.展开更多
Computer aided process planning（CAPP） is an important content of computer integrated manufacturing, and intelligentizing is the orientation of development of CAPP. Process planning has characters of empirical and ti...Computer aided process planning（CAPP） is an important content of computer integrated manufacturing, and intelligentizing is the orientation of development of CAPP. Process planning has characters of empirical and time-consuming to finalize, and the same technical aim always can be achieved by different process schemes, so intelligentizing of process decision making always be a difficult point of CAPP and computer integrated manufacturing （CIM）. For the purpose of intelligent aided process decision making and reuse of process resource, this paper proposed a decision making method based on rough sets（RS） and regular distance computing. The main contents and methods of process planning decision making are analyzed under agile response manufacturing environment, the concept of process knowledge granule is represented, and the methods of process knowledge granule partitioning and granularity analysis are put forward. Based on the theory of RS and combined the method of process attributes importance identification, the paper brought forward a computing model for process scheme regulation distance under the same attribute conditions, and conflict resolution strategy was introduced to acquire process scheme fit for actual situation of enterprise＇s manufacturing resources, so as to realize process resources＇ conflict resolution and quick excavate and reuse of enterprises＇ existing process knowledge, to advance measures of process decision making and improve the rationality and capability of agile response of process planning.展开更多
[Objective] This study aimed to improve classification accuracy of RS images using rough set theory in the growth of crops. [Method] Technique methods of data mining and knowledge discovery have been used. The develop...[Objective] This study aimed to improve classification accuracy of RS images using rough set theory in the growth of crops. [Method] Technique methods of data mining and knowledge discovery have been used. The development status of spatial data mining and knowledge discovery （SDMKD） is presented and data mining techniques in remote sensing were deeply analyzed. Then, SDMKD of TM image are researched using method of rough set, mainly including four methods （rough set, apriori algorithms, inductive learning, clustering）. [Result] The proposed method raises efficiency of land use and land reclaim. Based on the SDMKD, the characteristics of TM showed that the information after using rough set is more intensive than that of none. Especially, much better results can be gained while kinds of corps are less than five. [Conclusion] This study laid significant basis for further research on data mining in the growth of crops.展开更多
Rough set theory is a new soft computing tool, and has received much attention of researchers around the world. It can deal with incomplete and uncertain information. Now, it has been applied in many areas successfull...Rough set theory is a new soft computing tool, and has received much attention of researchers around the world. It can deal with incomplete and uncertain information. Now, it has been applied in many areas successfully. This paper introduces the basic concepts of rough set and discusses its applications in Web mining. In particular, some applications of rough set theory to intelligent information processing are emphasized.展开更多
In this paper, rough set theory is used to extract roughly-correct inference rules from information systems. Based on this idea, the learning algorithm ERCR is presented. In order to refine the learned roughly-correct...In this paper, rough set theory is used to extract roughly-correct inference rules from information systems. Based on this idea, the learning algorithm ERCR is presented. In order to refine the learned roughly-correct inference rules, the knowledge-based neural network is used. The method presented here sufficiently combines the advanages of rough set theory and neural network.展开更多
文摘Decision trees induction algorithms have been used for classification in a wide range of application domains. In the process of constructing a tree, the criteria of selecting test attributes will influence the classification accuracy of the tree.In this paper,the degree of dependency of decision attribute to condition attribute,based on rough set theory,is used as a heuristic for selecting the attribute that will best separate the samples into individual classes.The result of an example shows that compared with the entropy-based approach,our approach is a better way to select nodes for constructing decision trees.
文摘To improve the performance of the multiple classifier system, a new method of feature-decision level fusion is proposed based on knowledge discovery. In the new method, the base classifiers operate on different feature spaces and their types depend on different measures of between-class separability. The uncertainty measures corresponding to each output of each base classifier are induced from the established decision tables （DTs） in the form of mass function in the Dempster-Shafer theory （DST）. Furthermore, an effective fusion framework is built at the feature-decision level on the basis of a generalized rough set model and the DST. The experiment for the classification of hyperspectral remote sensing images shows that the performance of the classification can be improved by the proposed method compared with that of plurality voting （PV）.
基金This research received funding from Taif University,Researchers Supporting and Project Number(TURSP-2020/207),Taif University,Taif,Saudi Arabia.
文摘In this paper,we present a proposed method for generating a soft rough approximation as a modification and generalization of Zhaowen et al.approach.Comparisons were obtained between our approach and the previous study and also.Eventually,an application on Coronavirus(COVID-19)has been presented,illustrated using our proposed concept,and some influencing results for symptoms of Coronavirus patients have been deduced.Moreover,following these concepts,we construct an algorithm and apply it to a decision-making problem to demonstrate the applicability of our proposed approach.Finally,a proposed approach that competes with others has been obtained,as well as realistic results for patients with Coronavirus.Moreover,we used MATLAB programming to obtain the results;these results are consistent with those of theWorld Health Organization and an accurate proposal competing with the method of Zhaowen et al.has been studied.Therefore,it is recommended that our proposed concept be used in future decision making.
基金National Natural Science Foundation of China(No.51175077)
文摘In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.
基金supported by National Key Technology R&D Program of China (Grant No. 2006BAF01A07)National Hi-tech Research and Development Program of China (863 Program, Grant No. 2007AA04Z190)
文摘Computer aided process planning（CAPP） is an important content of computer integrated manufacturing, and intelligentizing is the orientation of development of CAPP. Process planning has characters of empirical and time-consuming to finalize, and the same technical aim always can be achieved by different process schemes, so intelligentizing of process decision making always be a difficult point of CAPP and computer integrated manufacturing （CIM）. For the purpose of intelligent aided process decision making and reuse of process resource, this paper proposed a decision making method based on rough sets（RS） and regular distance computing. The main contents and methods of process planning decision making are analyzed under agile response manufacturing environment, the concept of process knowledge granule is represented, and the methods of process knowledge granule partitioning and granularity analysis are put forward. Based on the theory of RS and combined the method of process attributes importance identification, the paper brought forward a computing model for process scheme regulation distance under the same attribute conditions, and conflict resolution strategy was introduced to acquire process scheme fit for actual situation of enterprise＇s manufacturing resources, so as to realize process resources＇ conflict resolution and quick excavate and reuse of enterprises＇ existing process knowledge, to advance measures of process decision making and improve the rationality and capability of agile response of process planning.
基金Supported by the by Research Fund for the Doctoral Program of Higher Education of China(20096121120001)Science Research Program of Educational Commission of Shaanxi Province of China(12JK0781)~~
文摘[Objective] This study aimed to improve classification accuracy of RS images using rough set theory in the growth of crops. [Method] Technique methods of data mining and knowledge discovery have been used. The development status of spatial data mining and knowledge discovery （SDMKD） is presented and data mining techniques in remote sensing were deeply analyzed. Then, SDMKD of TM image are researched using method of rough set, mainly including four methods （rough set, apriori algorithms, inductive learning, clustering）. [Result] The proposed method raises efficiency of land use and land reclaim. Based on the SDMKD, the characteristics of TM showed that the information after using rough set is more intensive than that of none. Especially, much better results can be gained while kinds of corps are less than five. [Conclusion] This study laid significant basis for further research on data mining in the growth of crops.
文摘Rough set theory is a new soft computing tool, and has received much attention of researchers around the world. It can deal with incomplete and uncertain information. Now, it has been applied in many areas successfully. This paper introduces the basic concepts of rough set and discusses its applications in Web mining. In particular, some applications of rough set theory to intelligent information processing are emphasized.
文摘In this paper, rough set theory is used to extract roughly-correct inference rules from information systems. Based on this idea, the learning algorithm ERCR is presented. In order to refine the learned roughly-correct inference rules, the knowledge-based neural network is used. The method presented here sufficiently combines the advanages of rough set theory and neural network.
