| Home  | About ScienceAsia  | Publication charge  | Advertise with us  | Subscription for printed version  | Contact us  
Editorial Board
Journal Policy
Instructions for Authors
Online submission
Author Login
Reviewer Login
Volume 48 Number 6
Volume 48 Number 5
Volume 48 Number 4
Volume 48 Number 3
Volume 48 Number 2
Volume 48S Number 1
Earlier issues
Volume 41 Number 6 Volume 42 Number 1

previous article next article

Research articles

ScienceAsia 42 (2016): 52-60 |doi: 10.2306/scienceasia1513-1874.2016.42.052

Text classification using similarity measures on intuitionistic fuzzy sets


ABSTRACT:     An intuitionistic fuzzy set (IFS) is an extended version of a fuzzy set and is capable of representing hesitancy degrees. A framework for text classification is presented. Two main challenges are addressed: how to represent documents in terms of IFSs and how to obtain a pattern of each class from such an IFS-based representation. By using some existing similarity measures for IFSs, the proposed framework is applied to two benchmark datasets for text classification. The proposed framework yields satisfactory results when compared to decision tree, k-NN, naïve Bayes, and support vector machine classifiers.

Download PDF

43 Downloads 1178 Views

Department˙of˙Mathematics˙and˙Statistics, Faculty˙of˙Science˙and˙Technology, Thammasat˙University, Pathum˙Thani˙12121˙Thailand

* Corresponding author, E-mail: peerasak@mathstat.sci.tu.ac.th

Received 14 Jul 2014, Accepted 17 Aug 2015