一种新的不均衡关联分类算法

New Associative Classification Algorithm for Imbalanced Data

基于规则的分类算法具有分类性能好、可解释性强的优点,得到了广泛的应用。然而已有的基于规则的分类算法没有考虑不均衡数据的情况,从而影响了其对不均衡数据的分类效果。文中提出了一种新的不均衡关联分类算法ACI。首先生成所有的关联规则,然后使用不均衡规则裁剪方法进行规则裁剪。最后,将剩余规则存储到CR树中,用于新实例的分类。在27个公开数据集上的实验结果表明,提出的不均衡关联分类算法在不均衡数据集上比基准算法的分类效果更好。

The rule-based classification algorithms,which have good classification performance and interpretability,have been widely used.However,the existing rule-based classification algorithms do not consider the case of imbalanced data,thus affect their classification effect on imbalanced data.In this paper,a new associative classification algorithm ACI for imbalanced data is proposed.Firstly,all the association rules are generated.Then,the rules are pruned by an imbalanced rule pruning method.Finally,the remaining rules are saved in a CR Tree for new instance classification.Experimental results on 27 public data sets show that the proposed algorithm performs better than the compared algorithms.