计算机科学 ›› 2011, Vol. 38 ›› Issue (11): 204-205.

• 人工智能 • 上一篇    下一篇

一种新的兼类文本分类方法

秦玉平,陈一荻,王春立,王秀坤   

  1. (渤海大学工学院 锦州121000) (大连海事大学信息科学技术学院 大连116026)(大连理工大学计算机科学与技术学院 大连116024)
  • 出版日期:2018-12-01 发布日期:2018-12-01

New Multi-label Text Classification Algorithm

  • Online:2018-12-01 Published:2018-12-01

摘要: 提出了一种基于超椭球的兼类文本分类算法。对每一类样本,在特征空间求得一个包围该类样本的最小超椭球,使得各类样本之间通过超椭球隔开。对待分类样本,通过判断其是否在超椭球内确定其类别。若没有超椭球包围待分类样本,则通过隶属度确定其所属类别。在标准数据集Reuters 21578上的实验结果表明,该方法较超球方法提高了分类精度和分类速度。

关键词: 超椭球,兼类分类,缩放因子,隶属度

Abstract: A new multi-label text classification algorithm based on hyper ellipsoidal was proposed in this paper. For every class, the smallest hyper ellipsoidal that contains the samples of the class is structured, which can divide the class samples from others. For the sample to be classified, its class is confirmed by the hyper ellipsoidal that surrounds it. If the sample is not surrounded by any hyper ellipsoidal, the membership is used to confirmed its class. The experiments were done on Reuters 21578 and the experiment results show that the algorithm has a higher performance on classificalion speed and classification precision compare with hyper sphere algorithm.

Key words: Hyper ellipsoidal, Multi-label classification, Extension factor, Membership

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