Abstract
Abstract Attribute reduction is a key issue in the research of rough sets. Aiming at the shortcoming of attribute reduction algorithm based on discernibility matrix, an attribute reduction method based on sample extraction and priority is presented. Firstly, equivalence classes are divided using quick sort for computing compressed decision table. Secondly, important samples are extracted from compressed decision table using iterative self-organizing data analysis technique algorithm(ISODATA). Finally, attribute reduction of sample decision table is conducted based on the concept of priority. Experimental results show that the attribute reduction method based on sample extraction and priority can significantly reduce the overall execution time and improve the reduction efficiency.
Highlights
Rough sets theory is a mathematical tool to deal with vague and uncertain knowledge presented by Pawlak in 1982 [1]
There are two main classes, the algorithms that are based on the discernibility matrices and the algorithms that are based on positive region
According to the above scholars’ researches, an attribute reduction method based on sample extraction and priority is presented in this paper
Summary
Rough sets theory is a mathematical tool to deal with vague and uncertain knowledge presented by Pawlak in 1982 [1]. It can mine the hidden knowledge from the mass data and has been successfully applied in fields such as data mining, pattern recognition, decision support system [2,3], etc. In recent years, it is aslo used in big data [4], information sharing [5], and other fields. There are two main classes, the algorithms that are based on the discernibility matrices and the algorithms that are based on positive region
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