关键词: 粗糙度，近似概念格，集成学习，分类挖掘

Abstract: Concept lattice is an effective tool for data classification, but classification efficiency and precision arc effected by its large scale. In this paper, rough sets theory was applied into the classification research of concept lattice, and a dynamical classification model(named CACLR)for ensemble learning based on approximation concept lattice of roughness was put forward. I}his model can constructs some identical approximation concept lattice classifiers of independent distribution and much precision according to the instance spatial configuration at the scope of roughness. And it can eliminate independent nodes in time during approximation concept lattice constructed, reduce the scale of concept lattice effestively. The multi-combination model for ensemble learning has robustness at the accuracy of rough classification and the efficiency of knowledge prediction. In the last part of this paper, the experiments tested on the UCI benchmark data sets were carried on and performance results of were given, which prove the practical value of CACLR model.

Key words: Roughness, Approximation concept lattice, Ensemble learning, Classification mining