Neighborhood rough set ensemble learning method based on attribute clustering

Abstract

The invention requests to protect a neighborhood rough set ensemble learning method based on attribute clustering, and relates to a data mining technology. First, the condition attributes of a decision system are divided into multiple clusters through attribute clustering, wherein the correlation between the attributes in the attribute clusters is large, and the correlation between different attribute clusters is small; second, different base classifiers are trained and integrated based on the difference between the clusters, guidance of a neighborhood rough set is added to the process of base classifier training and integrating, and the weights of the base classifiers are allocated according to the identification ability of the base classifiers to samples in the boundary region of the neighborhood rough set so as to get a final integrated classifier; and finally, test sets are classified by the obtained integrated classifier. According to the invention, a neighborhood rough set and the theory of ensemble learning are combined, the correlation and the difference between the condition attributes in a decision table are fully utilized, different base classifiers complement each other, and the knowledge in the decision system can be mined effectively from different angles.

Description

technical field [0001] The invention belongs to the field of data mining and pattern recognition, in particular to a neighborhood rough set integrated learning method after attribute clustering is carried out by using attribute correlation. Background technique [0002] The attributes of information systems in real life are not only diverse, but also often have certain correlations among attributes. If a single data mining algorithm is used directly for knowledge discovery, the effect is often not good. For this reason, methods such as attribute clustering and ensemble learning are used in data mining, which can effectively improve the effect of knowledge discovery. [0003] Attribute clustering: Clustering algorithm is an important method of unsupervised pattern recognition. It groups the sample space according to a certain similarity measure, so that the data in the group are similar to each other, and the similarity distance between the data in the group is large, so that...

AI Technical Summary

Problems solved by technology

The neighborhood rough set model is an extended model of the classic rough set theory by Hu Qinghua et al. using the neighborhood model, which solves the problem that the traditional rough set model cannot handle discrete data

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