摘要
当训练样本集规模过大时,最近邻分类规则约减过程是一个耗时的过程.目前,常见的约减算法往往存在计算成本过高、约减过程难于并行化等问题.针对该问题,文中将人工内分泌机制引入到最近邻规则的约减过程中,保留不同类规则边界上的边界规则,规则的约减规模通过晶格的粒度来设定.该方法可以在分割–约减–合并框架下获得较高的一致性约减子集,从而使规则的约减过程并行化,缩短约减时间.用11个不同的数据集进行仿真实验的结果显示,该方法简单而有效,较好地解决了大样本集的约减问题.
The main disadvantage in most prototype reduction algorithms is the excessive computational cost especially when the prototype size is large. To deal with the problem, we present a new prototype reduction method in which an artificial endocrine system is embedded. The method remains only for points on boundaries between different classes. The amount of reduced rules of the reference set can be revised by granularity of the lattice. The proposed method can get a consistent subset in a divide-reduce-coalesce manner, making it more efficient and effective than other algorithms. The proposed approach has been tested using 11 different datasets. The experiments show that the algorithm can give correct results when the size of data.set is large.
出处
《应用科学学报》
EI
CAS
CSCD
北大核心
2012年第4期397-407,共11页
Journal of Applied Sciences
基金
国家自然科学基金(No.60873035
No.61073091)
陕西省自然科学基金(No.2010JM8028)
西安理工大学优秀博士学位论文研究基金(No.116-211102)资助
关键词
最近邻规则
人工内分泌机制
约减
一致性子集
nearest neighbor rule
artificial endocrine system
condensation
consistent subset
