基于粒子群优化的快速KNN分类算法

提出了一种有效的快速ｋ近邻分类文本分类算法,即PSOKNN算法，该算法利用粒子群优化方法的随机搜索能力在训练文档集中进行有指导的全局随机搜索. 在搜索ｋ近邻的过程中，粒子群跳跃式移动，掠过大量不可能成为ｋ近邻的文档向量，从而可以快速找到测试样本的ｋ个近邻. 以Reuters 21578文档集分类为例验证算法的有效性，结果表明，保持ｋ近邻法分类精度，新算法比KNN算法降低分类时间70％.

An improved KNN classification algorithm based on particle swarm optimization

An efficient algorithm PSOKNN is proposed to reduce the computational complexity of KNN text classification algorithm, it is based on particle swarm optimization which has random and directed global search ability to search randomly and directed within training document set. During the procedure for searching k nearest neighbors of tested sample, the particle swarm moves jumpily, and those document vectors that are impossible to be the k closest vectors are kicked out quickly. By dassif-ying Reuters-21578, the veracity of KNNPSO is the same as that of KNN, and PSOKNN reduces approximate 70% classification than KNN.