随着LAMOST巡天的逐步实施, 天体光谱数据量极大, 对观测数据进行自动分类及分析具有重要的意义。 采用常规方法获取的分类规则集中, 往往存在大量冗余规则, 影响了分类效率和质量。 本文给出了一种基于谓词逻辑的分类规则后处理方法, 通过利用谓词描述光谱分类规则, 并对分类规则集进行谓词演算, 消除冗余规则。 最后, 采用LAMOST观测的恒星光谱数据, 实验验证该方法在保证分类准确率不降低的前提下, 可大幅提高自动分类效率。

Automatic classification and analysis of observational data is of great significance along with the gradual implementation of LAMOST Survey, which will obtain a large number of spectra data. In classification rules extracted, there is often a great deal of redundancy which will reduce the classification efficiency and quality seriously. In the present paper, a post-processing method of star spectra classification rule based on predicate logic is presented by using predication to describe the classification rules and logical reasoning to eliminate redundant rules. In the end, some experimental results on LAMOST’s stellar spectra data show that, with no classification accuracy reduction, the efficiency of auto classification is significantly improved.