基于支持向量机的兼类文本分类算法研究

Study on multi-subject text classification algorithm based on support vector machines

针对兼类文本,提出了两种基于支持向量的分类算法。一种是采用1-a-1方法训练子分类器,通过子分类器得到待分类样本的隶属度矩阵,依据隶属度矩阵每行元素和判定该文本所属类别。另一种是采用1-a-r方法训练子分类器,通过子分类器得到待分类样本的隶属度向量,根据隶属度向量判定该文本所属的类别。实验结果表明,这两种算法都具有较好的准确率、召回率和F1值。

For multi-subject text, two classification algorithms based on support vector machines are proposed. The first method uses 1-a-1 to train sub-classifiers, for the samples to be classified, sub-classifiers are used to obtain membership matrix, and then according to the sum of every line of membership matrix, confirms the subjects that the sample belongs to. The second method uses 1-a-r to train sub-classifiers, for the samples to be classified, sub-classifiers are used to obtain the membership vector, according to the membership vector, confirms the subjects that the sample belongs to. The experimental results show that the proposed algorithms have higher performance on precision, recall and F 1 value.