基于粒子群优化的数据分类算法

Particle Swarm Optimization for Data Classification

设计了一种基于粒子群优化的数据分类算法。新算法首先对数据样本预处理,利用粒子群优化算法通过训练数据进行分类规则的提取,根据提取得到的规则对数据进行分类识别。基于Bayes定理和随机状态转移过程对新算法的收敛性进行分析。通过对UCI数据集分类实验及遥感图像目标识别实验,验证了新算法是一种有效的分类方法。

A novel data classification method, Particle Swarm Optimization for classification （PSOC）, was put forward. After pretreating the data, the classification rules were discovered by particle swarm optimization algorithm based on the training samples, and then the data was classified by the discovered rules. The convergence of the new algorithm was analyzed based on Bayes’s theorem and stochastic transform process. Experimental study on UCI machine learning repository and the remote sensing image data shows the proposed algorithm obtains good performances.