Authors: Sambhav Jain; Shuvo Saha Roy; Reshma Rastogi
In this paper, a new variant of Twin Support Vector Machines (TSVM) termed as Neo-Twin Support Vector Machines (Neo-TSVM) has been proposed for binary pattern classification. TSVM uses hinge loss to allow optimal separation from the opposite class, casting it as a constrained optimisation problem. Neo-TSVM presents a simpler model which eliminates the constraints and cast it as an Unconstrained Minimisation Problem (UMP). Further to allow, better separation between the non-parallel hyperplanes, the notion of angle has also been introduced in the optimisation problem. For testing the efficacy of the proposed classifier, experiments have been conducted on benchmark datasets, and it is observed that the proposed classifier achieves results comparable to that of TSVM, and is also time efficient.
Published in: 2022 International Conference on Decision Aid Sciences and Applications (DASA)
Date of Conference: 23-25 March 2022
Date Added to IEEE Xplore: 02 May 2022
ISBN Information:
Electronic ISBN:978-1-6654-9501-1
Print on Demand(PoD) ISBN:978-1-6654-9502-8
INSPEC Accession Number: 21709974
DOI: 10.1109/DASA54658.2022.9765240
Publisher: IEEE
Conference Location: Chiangrai, Thailand
https://ieeexplore.ieee.org/document/9765240