计算机科学 ›› 2017, Vol. 44 ›› Issue (8): 207-215.doi: 10.11896/j.issn.1002-137X.2017.08.036
李贞,张卓,王黎明
LI Zhen, ZHANG Zhuo and WANG Li-ming
摘要: 随着网络中三维数据的涌现,三元概念分析的优势也逐渐体现出来。三元概念分析是较新的研究领域,具有广阔的发展前景。提出基于三元概念分析的文本分类方法,该方法是一种全新的构思理念,是三元概念分析在应用上的拓展。该算法的主要思路是:首先将数据集预处理为三元背景,同时将背景中的二值关系扩展为0-1间的模糊关系,其用于表示特定条件下属性对于对象的隶属度,并基于此构建三元概念,利用三元概念表示数据集中文本、特征词与类别之间的三元关系;然后结合模糊理论中的贴近度,类比得出三元概念间的相似度,并运用相似性度量计算出训练集中三元概念与新文本的相似值。实验结果表明,文中所提模型是有效的,且在特定的数据集上相较于机器学习Support Vector Machine(SVM)算法、K-Nearest Neighbor(KNN)算法、卷积神经网络(CNN)算法以及基于形式概念分析的分类模型均有更好的分类效果。
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