Title: A new term weighting scheme for text categorisation

Authors: Fatiha Barigou

Addresses: Laboratory of Computer Science of Oran, Department of Computer Science, University of Oran 1, Ahmed Ben Bella, Oran 31000, Algeria

Abstract: 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.

Keywords: text categorisation; supervised term weighting; kNN; k-nearest neighbour; supervised learning; training documents; term selection; classifiers.

DOI: 10.1504/IJISTA.2015.074332

International Journal of Intelligent Systems Technologies and Applications, 2015 Vol.14 No.3/4, pp.256 - 272

Received: 11 May 2015
Accepted: 27 Oct 2015

Published online: 22 Jan 2016 *

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