计算机科学 ›› 2023, Vol. 50 ›› Issue (2): 166-172.doi: 10.11896/jsjkx.211200292
王笑笑, 巴婧, 陈建军, 宋晶晶, 杨习贝
WANG Xiaoxiao, BA Jing, CHEN Jianjun, SONG Jingjing, YANG Xibei
摘要： 利用多重约简的结果搭建一个集成分类框架,已被证实可以显著提升后续学习的性能。超约简方法正是借鉴了这一理念,在约简求解的基础上,通过随机添加额外属性以达到获取多重超约简的目的。显然,基本的约简求解将直接影响超约简方法的效果。鉴于此,从兼顾效率和性能的角度出发,在超约简方法中同时引入属性簇和集成选择机制:属性簇用于加速基本约简的求解过程,集成选择则用于在求解过程中找到更为稳健的属性。在20组UCI数据上的实验结果表明,相比4种前沿的集成策略,所提方法不仅能够显著减少约简求解的时间消耗,而且能够提供更好的分类稳定性和准确率。
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