Rough Set Based Approach to Selection of Node
نویسنده
چکیده
Decision tree is widely used in machine learning. In the process of constructing a tree, appropriate attributes have to be selected as nodes of the tree based on some criteria. There are several approaches to selection of attributes. In this paper, we present a new approach to selection of attributes for construction of decision tree based on rough set theory. The basic idea is, if the size of the implicit region corresponding to one condition attribute is the smallest, then this attribute will be chosen as the node for branching. The presented method is different from those methods for finding reduct of attributes in which rough set theory have been used to select a subset of attributes from a given attribute set and thus reduce the volume of data to be treated. Comparison between the present method and the entropy-based method shows that the rough set-based approach is a feasible way to selection of nodes of decision tree. c ©2002 Yang’s Scientific Research Institute, LLC. All rights reserved.
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