基于关联规则的特征选择算法

Feature Selection Algorithm Based on Association Rules

关联规则能够发现数据库中属性之间的关联,通过优先选择短规则用于相关属性的选择,有可能得到最小的属性子集.基于此,本文提出一种基于关联规则的特征选择算法,实验结果表明在属性子集大小和分类精度上优于多种特征选择方法.同时,对支持度和置信度对算法效果的影响进行探索,结果表明高的支持度和置信度并不导致高的分类精度和小的特征子集,而充足的规则数是基于关联规则特征选择算法高效的必要条件.

A feature selection algorithm based on association rules is presented, and the impact of support and confidence on the presented method are studied. The experimental results show that the feature subset size and classification accuracy of the presented method are better than those of other methods. Furthermore, the results indicate high support and confidence levels do not guarantee high classification accuracy and small feature subset, and the sufficient number of rules is the precondition for high efficiency of feature selection based on association rules.