Data discretization contributes much to the induction of classification rules or trees by machine learning methods.The rough set theory is a valid tool for discretizing continuous information systems.Herein,a new meth...Data discretization contributes much to the induction of classification rules or trees by machine learning methods.The rough set theory is a valid tool for discretizing continuous information systems.Herein,a new method is proposed to improve those typical rough set based heuristic algorithms for data discretization,by utilizing decision information to reduce the scales of candidate cuts,and by more reasonably measuring cut significance with a new conception of cut selection probability.Simulations demonstrate that compared with other typical discretization algorithms based on the rough set theory,the proposed method is more capable and valid to discretize continuous information systems.It can effectively improve the predictive accuracies of information systems while still conceptually keeping their consistency.展开更多
Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and d...Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and distance measure, and were proved. To calculate the degree of similarity of discrete data, relative degree between data and total distribution was obtained. Discrete data similarity measure was completed with combination of mentioned relative degrees. Power interconnected system with multi characteristics was considered to apply discrete similarity measure. Naturally, similarity measure was extended to multi-dimensional similarity measure case, and applied to bus clustering problem.展开更多
Analytical solutions have varied uses. One is to provide solutions that can be used in verification of numerical methods. Another is to provide relatively simple forms of exact solutions that can be used in estimating...Analytical solutions have varied uses. One is to provide solutions that can be used in verification of numerical methods. Another is to provide relatively simple forms of exact solutions that can be used in estimating parameters, thus, it is possible to reduce computation time in comparison with numerical methods. In this paper, an alternative procedure is presented. Here is used a hybrid solution based on Green＇s function and real characteristics （discrete data） of the boundary conditions.展开更多
Study of fuzzy entropy and similarity measure on intuitionistic fuzzy sets (IFSs) was proposed and analyzed. Unlike fuzzy set, IFSs contain uncertainty named hesitance, which is contained in fuzzy membership function ...Study of fuzzy entropy and similarity measure on intuitionistic fuzzy sets (IFSs) was proposed and analyzed. Unlike fuzzy set, IFSs contain uncertainty named hesitance, which is contained in fuzzy membership function itself. Hence, designing fuzzy entropy is not easy because of many entropy definitions. By considering different fuzzy entropy definitions, fuzzy entropy on IFSs is designed and discussed. Similarity measure was also presented and its usefulness was verified to evaluate degree of similarity.展开更多
The governing factors that influence landslide occurrences are complicated by the different soil conditions at various sites.To resolve the problem,this study focused on spatial information technology to collect data ...The governing factors that influence landslide occurrences are complicated by the different soil conditions at various sites.To resolve the problem,this study focused on spatial information technology to collect data and information on geology.GIS,remote sensing and digital elevation model（DEM） were used in combination to extract the attribute values of the surface material in the vast study area of SheiPa National Park,Taiwan.The factors influencing landslides were collected and quantification values computed.The major soil component of loam and gravel in the Shei-Pa area resulted in different landslide problems.The major factors were successfully extracted from the influencing factors.Finally,the discrete rough set（DRS） classifier was used as a tool to find the threshold of each attribute contributing to landslide occurrence,based upon the knowledge database.This rule-based knowledge database provides an effective and urgent system to manage landslides.NDVI（Normalized Difference Vegetation Index）,VI（Vegetation Index）,elevation,and distance from the road are the four major influencing factors for landslide occurrence.The landslide hazard potential diagrams（landslide susceptibility maps） were drawn and a rational accuracy rate of landslide was calculated.This study thus offers a systematic solution to the investigation of landslide disasters.展开更多
基金supported by the Program for New Century Excellent Talents in University, National Natural Science Foundation of China (60573068, 60773113)Natural Science Foundation of Chongqing of China (2005BA2003, 2008BA2017)+1 种基金Starting Research Foundation of Ministry of Education for Chinese Overseas Returnees ([2007]1108)Scientific Research Foundation of Chongqing University of Posts and Telecommunications (A2006-05)
文摘Data discretization contributes much to the induction of classification rules or trees by machine learning methods.The rough set theory is a valid tool for discretizing continuous information systems.Herein,a new method is proposed to improve those typical rough set based heuristic algorithms for data discretization,by utilizing decision information to reduce the scales of candidate cuts,and by more reasonably measuring cut significance with a new conception of cut selection probability.Simulations demonstrate that compared with other typical discretization algorithms based on the rough set theory,the proposed method is more capable and valid to discretize continuous information systems.It can effectively improve the predictive accuracies of information systems while still conceptually keeping their consistency.
基金Project(2010-0020163) supported by Key Research Institute Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology, Korea
文摘Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and distance measure, and were proved. To calculate the degree of similarity of discrete data, relative degree between data and total distribution was obtained. Discrete data similarity measure was completed with combination of mentioned relative degrees. Power interconnected system with multi characteristics was considered to apply discrete similarity measure. Naturally, similarity measure was extended to multi-dimensional similarity measure case, and applied to bus clustering problem.
文摘Analytical solutions have varied uses. One is to provide solutions that can be used in verification of numerical methods. Another is to provide relatively simple forms of exact solutions that can be used in estimating parameters, thus, it is possible to reduce computation time in comparison with numerical methods. In this paper, an alternative procedure is presented. Here is used a hybrid solution based on Green＇s function and real characteristics （discrete data） of the boundary conditions.
基金Project(ER120001) supported by Development of Application Technology BioNano Super Composites, Korea
文摘Study of fuzzy entropy and similarity measure on intuitionistic fuzzy sets (IFSs) was proposed and analyzed. Unlike fuzzy set, IFSs contain uncertainty named hesitance, which is contained in fuzzy membership function itself. Hence, designing fuzzy entropy is not easy because of many entropy definitions. By considering different fuzzy entropy definitions, fuzzy entropy on IFSs is designed and discussed. Similarity measure was also presented and its usefulness was verified to evaluate degree of similarity.
基金National Science Council(102-2313-b-275-001),which sponsored this work
文摘The governing factors that influence landslide occurrences are complicated by the different soil conditions at various sites.To resolve the problem,this study focused on spatial information technology to collect data and information on geology.GIS,remote sensing and digital elevation model（DEM） were used in combination to extract the attribute values of the surface material in the vast study area of SheiPa National Park,Taiwan.The factors influencing landslides were collected and quantification values computed.The major soil component of loam and gravel in the Shei-Pa area resulted in different landslide problems.The major factors were successfully extracted from the influencing factors.Finally,the discrete rough set（DRS） classifier was used as a tool to find the threshold of each attribute contributing to landslide occurrence,based upon the knowledge database.This rule-based knowledge database provides an effective and urgent system to manage landslides.NDVI（Normalized Difference Vegetation Index）,VI（Vegetation Index）,elevation,and distance from the road are the four major influencing factors for landslide occurrence.The landslide hazard potential diagrams（landslide susceptibility maps） were drawn and a rational accuracy rate of landslide was calculated.This study thus offers a systematic solution to the investigation of landslide disasters.
