Theory of rough sets, proposed by Zdzislaw Pawlak in 1982, is a model of approximate reasoning. In applications, rough set methodology focuses on approximate representation of knowledge derivable from data. It leads t...Theory of rough sets, proposed by Zdzislaw Pawlak in 1982, is a model of approximate reasoning. In applications, rough set methodology focuses on approximate representation of knowledge derivable from data. It leads to significant results in many areas including, for example, finance, industry, multimedia, medicine, and most recently bioinformatics.展开更多
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
文摘Theory of rough sets, proposed by Zdzislaw Pawlak in 1982, is a model of approximate reasoning. In applications, rough set methodology focuses on approximate representation of knowledge derivable from data. It leads to significant results in many areas including, for example, finance, industry, multimedia, medicine, and most recently bioinformatics.
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.
