Text classification based on inter-class separability DAG-SVM
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摘要: 本方法采用了以类间分布和类间中心距离作为依据，对有向无环图结构进行调整，以解决传统的DAG-SVM多分类结构固定、单个节点位置随意引起的误差累积严重的缺陷.实验表明，该改进后的DAG-SVM文本分类方法，对文本分类准确率有一定的提高.Abstract: This paper took an improved algorithm based on inter-class separability directed acyclic graph support vector machine (DAG-SVM) for text classification.The method has adjusted the DAG structure according to inter-class distribution and the distance between centers. It has solved the problems of fixed structure and random single node location in traditional DAG-SVM multi-classification method.The experiments show that the algorithm has improved the accuracy.
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Key words:
- text classification /
- support vector machine /
- DAG-SVM /
- inter-class separability
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