A new term weighting scheme for text categorisation

被引：0

作者：

Barigou, Fatiha ^{[1
]}

机构：

[1] Laboratory of Computer Science of Oran, Department of Computer Science, University of Oran, 1, Ahmed Ben Bella, Oran

来源：

International Journal of Intelligent Systems Technologies and Applications | 2015年 / 14卷 / 3-4期

关键词：

K nearest neighbours; KNN; Supervised term weighting scheme; Term weighting; Text categorisation;

D O I：

10.1504/IJISTA.2015.074332

中图分类号：

学科分类号：

摘要：

Recently, the study of term weighting schemes has increasingly attracted the attention of researchers in the field of text categorisation (TC). Unlike information retrieval, TC is a supervised learning task that makes use of the prior information about the distribution of training documents in different predefined categories. This information, being omitted from traditional weighting schemes, is considered very useful and has been widely used for the term selection and building classifiers. This paper aims to study and analyse a new weighting measure to improve performance of a k nearest neighbours (kNN)-based TC. Copyright © 2015 Inderscience Enterprises Ltd.

引用

页码：256 / 272

页数：16

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