简化的粒子群优化快速KNN分类算法

提出了一种有效的k近邻分类文本分类算法，即SPSOKNN算法，该算法利用粒子群优化方法的随机搜索能力在训练集中随机搜索.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，并且去除了粒子群进化过程中粒子速度的影响，从而可以更快速地找到测试样本的k个近邻.通过验证算法的有效性表明，在查找k近邻相同时，SPOSKNN算法的分类精度高于基本KNN算法。

Improved simplified PSO KNN classification algorithm

An efficient algorithm SPSOKNN is proposed to reduce the computational complexity of KNN text classification algo- rithm,it is based on particle swarm optimization which searches randomly within training document set.During the procedure for searching k nearest neighbors of tested sample,those document vectors that are impossible to be the k closest vectors are kicked out quickly.And removing PSO evolutionary process of particle velocity impact,thus we can more rapidly find the k closest vec- tors of test samples.By verifying the validity of algorithm,finding the same k nearest neighbors,classification accuracy of SP- SOKNN algorithm is higher than KNN algorithm.