基于特征信息增益权重的文本分类算法

Classifying Text Corpus Based on Information Gain Weight of Feature

为了在分类精度不受损失的情况下提高训练速度,设计了3种基于信息增益(information gain,简称IG) 特征权重的分类算法,分别被命名为:IG-C1、IG-C2、IG-C．它们根据特征对IG贡献的大小及在新文本中出现的次数进行分类．这3种算法都具有较低的时间复杂度和实现简单的特点．实验结果表明,其中IG-C的分类效果最为理想．

In order to improve the training speed of classifiers without losing their accuracy, three classifying algorithms based on information gain of features are provided in this work. They are IG-C1, IG-C2 and IG-C, which classifies unlabeled text according to features＇ weight generated in feature selection phase. All these approaches have two characteristics： lower time complexity and simpler implementation. The performance comparison between these algorithms and Naive Bayes, Vector Space Model using retuers 21578 and 20 newsgroup data sets, shows that IG-C algorithm is best one.