基于随机森林的文本分类模型研究

随着WWW的迅猛发展，文本分类成为处理和组织大量文档数据的关键技术．随机森林模型是决策树的集成，并且由一随机向量决定决策树的构造．当森林中的决策树的数目增大，随机森林的泛化误差将趋向一个上界．将随机森林模型应用于文本分类，在Reuter21578数据集上的实验表明，分类效果比较好，性能比较稳定，将共同C4．5，KNN，SM0，SVM4种典型的文本分类器进行了比较，结果显示它的分类性能胜于CA．5，同KNN，SMO和SVM方法相当．

Automatic text classification model based on random forest

With the rapid development of World Wide Web, text classification has become the key technology in organizing and processing large amount of document data. Random forests are a combination of tree predictors such that each tree depends on the values of random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges to a limit as the number of trees in the forest becomes large. In experiments it is compared to CA. 5, KNN, SMO and SVM, and the results show that its performance is higher than CA.5 and comparable with KNN, SMO and SVM. It is a promising technique for text categorization.