基于非线性流形学习和支持向量机的文本分类算法

为解决文本自动分类问题,提出一种流形学习和支持向量机相结合的文本分类算法(LLE-LSSVM)。LLE-LSSVM算法利用非线性流形学习算法LEE对高维文本特征进行非线性降维,挖掘出特征内在规律与本征信息,从而得到低维特征空间,然后将其输入到LSSVM中进行学习,同时利用混沌粒子群算法对LSSVM参数进行优化,建立文本分类模型。仿真实验结果表明,LLE-LSSVM算法提高了文本分类准确率,减少了分类运行时间,是一种有效的文本分类算法。

Text Categorization Algorithm Based on Manifold Learning and Support Vector Machines

In order to solve the text classification problem, this paper put forward a text classification algorithm based on manifold learning and support vector machine (LLE-LSSVM). Firstly, high dimension text characteristics are reduced by LEE algorithm, and the inner rule and characteristics of the information are mined to obtain meaningful low-dimensional feature space. Secondly the features arc input into the I_SSVM to be learnt while using chaotic particle swarm algorithm to optimize LSSVM parameters. Lastly establishes the text classification model. The simulation results show that the proposed algorithm improves text classification accuracy and reduces the classification time, and it is an effective text classification algorithm.