Rough set-based confidence rule extraction method and system, and medium

Abstract

The invention discloses a rough set-confidence rule extraction method and system, and a medium. The implementation steps of the method comprise: establishing a knowledge expression system for an object of a confidence rule base and a data set thereof, determining the number of initial reference values, setting the initial width of a reference value interval as a fixed value, carrying out equivalent class division, calculating confidence, and extracting a confidence rule; Reasoning a confidence rule base established by the test set based on the extracted confidence rule; and optimizing the number of reference values and the reference value interval according to the reasoning result to obtain the optimal number of reference values and the reference value interval, then performing equivalentclass division based on the optimal number of reference values and the reference value interval, and calculating confidence to extract a confidence rule. According to the reasoning result, the initially determined attribute index is optimized, so that the optimal attribute index value can be obtained, and the reasoning precision of the confidence rule base system can be improved.

Description

technical field [0001] The invention relates to a confidence rule base technology, in particular to a method, system and medium for extracting confidence rules based on rough sets, which are used to realize the extraction of confidence rules based on rough sets. Background technique [0002] In order to effectively use quantitative information and incomplete or uncertain information provided by experts to model complex problems, Yang et al. proposed a belief rule-base inference method using the evidential reasoning approach , referred to as RIMER). The confidence rule base reasoning method combines the knowledge of D-S evidence theory, decision theory, fuzzy theory and traditional IF-THEN rule base, and can analyze data with fuzzy or fuzzy uncertainty, incomplete or probabilistic uncertainty, and nonlinear characteristics. for modeling. At present, the reasoning method of confidence rule base has been widely used in oil pipeline leak detection, fault diagnosis, intention r...

AI Technical Summary

Problems solved by technology

However, in the process of establishing the initial confidence rule base system, due to various reasons such as difficulty in obtaining expert experience and inconsistent standards, the establishment period of the initial confidence rule base system is often long, and the initial confidence rule base system does not match the actual system.

How to quickly and effectively establish an initial belief rule base system has become an urgent problem to be solved, but there are still relatively few studies in this area

Images