A k-Highest Expert Text Classification Algorithm Based on Choquet Integral

被引：2
|
作者：
Feng, Shuchao [1 ]
Shang, Wenqian [2 ]
Wang, Yuqi [2 ]
机构：
[1] Commun Univ China, Sch Sci, Beijing, Peoples R China
[2] Commun Univ China, Sch Comp Sci, Beijing, Peoples R China
来源：
3RD INTERNATIONAL CONFERENCE ON APPLIED COMPUTING AND INFORMATION TECHNOLOGY (ACIT 2015) 2ND INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND INTELLIGENCE (CSI 2015) | 2015年
关键词：
KNN; Choquet Integral; Cosine Similarity; Text Classification; Monotone Measure;
D O I：
10.1109/ACIT-CSI.2015.95
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In recent years, the research on text classification algorithm is still a hot topic in text mining. The KNN is a classic text classification algorithm. The rule of finding the nearest neighbors directly affects the performance and precision of categorization. In this paper, we mainly focus on distance measure and similarity. We propose a new text classification algorithm which combines KNN and Choquet integral. Choquet integral provides a new way to find the k-nearest neighbors. The result of experiment shows that the performance of this method is better than the classical distance measure or similarity measure.
引用
收藏
页码：499 / 503
页数：5
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