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Classification algorithm is a kind of important technology in data mining, and the most commonly used is decision tree learning. In the process of constructing a decision tree, the selecting criteria of splitting attributes will directly affect the classification results. And the attribute selection of the traditional decision tree algorithm is based on information theory. In this paper, by combining...
In this paper, for the refinement of the database in data mining, by synthetically analyzing the characteristics of the current attribute reduction methods and decision tree algorithm, we put forward formalized description model of rule knowledge, and establish a kind of attribute reduction method (BD-RED) of decision tree by using similarity between rules families. Further, we discuss the construction...
Fuzzy set theory and rough set theory are effective tools for dealing with incomplete and inaccurate knowledge of information systems. In this paper, for the difference of fuzzy equivalence relation matrix of the attributes in information system, we establish a minimum hybrid entropy method measuring the relation of the attributes, and propose a new attribute reduction method based on the minimum...
The attribute reduction of information system can improve the accuracy of knowledge discovery, machine learning, etc. and it also can improve the efficiency. This paper proposes an attribute testing reduction algorithm, the algorithm can make the information system retain as few as attributes under the condition that maintains the original style, it can not only save much time for the later system...
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