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New rough set approach to knowledge reduction in decision table

Jianmei Xiao, Tengfei Zhang

Open publisher page 9 citations

Abstract

The core and knowledge reduction of a decision table are the key points of many information process procedures. It has been proved that computing all the reductions and the optimal reduction of a decision table is a NP-complete problem. In this paper, the algorithms for finding relative core and relative knowledge reduction are presented, which are based on the positive region in rough set theory. The effectiveness of the algorithms is demonstrated by some typical examples.

About this research paper

What this paper is about

The core and knowledge reduction of a decision table are the key points of many information process procedures. It has been proved that computing all the reductions and the optimal reduction of a decision table is a NP-complete problem. In this paper, the algorithms for finding relative core and relative knowledge reduction are presented, which are based on the positive region in rough set theory. The effectiveness of the algorithms is demonstrated by some typical examples.

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OpenAlex reports 9 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Method / approach

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Available abstract

The core and knowledge reduction of a decision table are the key points of many information process procedures. It has been proved that computing all the reductions and the optimal reduction of a decision table is a NP-complete problem. In this paper, the algorithms for finding relative core and relative knowledge reduction are presented, which are based on the positive region in rough set theory. The effectiveness of the algorithms is demonstrated by some typical examples.

Key concepts: Decision table, Rough set, Reduction (mathematics), Table (database), Computer science, Core (optical fiber), Set (abstract data type), Key (lock)

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