人工内分泌机制在最近邻规则约减中的应用

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.

Nearest Neighbor Rule Condensation Algorithm Based on Artificial Endocrine System

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 effcient 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 dataset is large.