基于Boosting算法集成遗传模糊分类器的文本分类

提出一种新颖的基于Boosting模糊分类的文本分类方法。首先采用潜在语义索引(LSI)对文本特征进行选择；然后提出Boosting算法集成模糊分类器学习，在每轮迭代训练过程中，算法通过调整训练样本的分布，利用遗传算法产生分类规则。减少分类规则能够正确分类样本的权值，使得新产生的分类规则重点考虑难于分类的样本。实验结果表明，该文本分类算法具有良好分类的性能。

Text categorization based on genetic fuzzy classification and Boosting method

A novel method for text categorization, which is based on boosting fuzzy classification, was proposed in the paper. Latent Semantic Index (LSI) was used to select text feature and then Boosting algorithm was proposed to integrate fuzzy classification. In each iteration training of boosting algorithm, the distribution of training instances was adjusted, and classification rules were created by genetic algorithm. The weights of the training instances that were classified correctly by available rules were reduced, so that the new fuzzy rule focuses on the misestimate or uncovered instances. Experimental results show that classifier based on fuzzy classification is effective and efficient.