基于粒子群智能的中文文本分类模型比较研究

Comparative Study of Chinese Text Classification Model based on Particle Swarm Intelligence

面对海量、异构、动态的文本信息,对文本进行自动分类具有重要的意义。近年来,逐步发展起来的群集智能理论和方法为文本分类提供了一种新的智能化手段。笔者将群集智能中发展较为成熟的粒子群智能算法尝试性地引入到文本分类领域。构建了文本预处理模型,该模型是文本分类模型的基础。构建了基于PSO的文本分类模型Text PSO-Miner,并在文本集的向量空间矩阵上进行测试和比较。Text PSO-Miner的各项性能指标都优于经典的分类模型(SVM,KNN,NB)和基于ACO的文本分类模型。结果表明:Text PSO-Miner文本分类模型能够更好地应用于文本分类。

In the face of massive, heterogeneous, dynamic text information, automatic text classification is of great significance. In recent years, the swarm intelligence theory and method, which has been gradually developed, provides a new intelligent method for text categorization. This paper attempted to introduce the mature particle swarm intelligence algorithm to the text classification field. The text preprocessing model was constructed, which was the foundation of text categorization model. A text categorization model Text PSO-Miner based on PSO was constructed and tested and compared on the vector space matrix of text set. Text PSO-Miner performance indicators were better than the classic classification model（SVM, KNN, NB） and ACO based text classification model. The results showed that Text PSO-Miner can be better applied to text categorization.