A Matrix Incremental Reduction Method Based on Knowledge Granularity

Abstract

The invention discloses a matrix incremental reduction method based on knowledge granularity, which includes the following steps: first, calculate the minimum attribute reduction (REDU) of an existing decision table (called an old decision table) by using an equivalent relation matrix; After some new objects are added to the old decision table (called the new decision table), calculate the knowledge granularity of the minimum attribute reduction in the old decision table and the knowledge granularity of the new decision table through the matrix increment method, and judge whether they are equal, if not In the new decision table, the matrix increment method is used to calculate the external importance of all attributes except REDU, and the attribute with the largest external importance is selected in turn to add to REDU, and then the knowledge granularity of REDU is calculated until it is consistent with Until the knowledge granularity of the new decision table is equal; finally, redundant attributes in REDU are cyclically deleted to obtain the minimum attribute reduction of the new decision table. The invention effectively solves the problem of quickly solving the minimum attribute reduction when objects in the decision table dynamically increase, thereby helping to improve the efficiency of knowledge discovery.

Description

technical field [0001] The invention relates to the technical field of granular computing and rough set theory in artificial intelligence, in particular to a matrix incremental reduction method based on knowledge granularity. Background technique [0002] At present, with the continuous development and wide application of storage technology, information systems in practical applications not only accumulate a large amount of various data, but also these real-time data will change every moment. It takes a lot of time to mine complex data, which makes it difficult for batch learning algorithms to extract useful information and knowledge from dynamic data in a timely and efficient manner. Incremental learning is a learning system that can continuously learn new knowledge from new samples from the environment and retain most of the previous knowledge. By using incremental learning technology, knowledge can be updated gradually, and previous knowledge can be corrected and strengt...

AI Technical Summary

Problems solved by technology

[0002] At present, with the continuous development and wide application of storage technology, information systems in practical applications not only accumulate a large amount of various data, but also these real-time data will change every moment. It takes a lot of time to mine complex data, which makes it difficult for batch learning algorithms to extract useful information and knowledge from dynamic data in a timely and efficient manner.

Images