Title: XML document classification effectively using improved high-performance factor

Authors: S. Sahunthala; Angelina Geetha; Latha Parthiban

Addresses: Hindustan Institute of Technology and Science, Chennai, India ' Hindustan Institute of Technology and Science, Chennai, India ' Department of Computer Science, Pondicherry University, India

Abstract: Nowadays, XML data plays in volume amount of business application. The real World Wide Web has more XML data in the website. The heterogeneous structure XML data classification is the challenging task in the research recently. Algorithms are available to classify the XML data by classification method. The performance is degraded in the classification XML document in the existing technique. In this paper, the machine learning technique tuning improved hyper parameter optimisation algorithm (TIHPOA) is proposed to classify the XML data. First, the elements are extracted by using feature extraction vector space model. Then the XML data is classified using the algorithm of TIHPOA technique. The proposed model uses the improved hyper parameters to generate the better classifier than the existing classification approach. In the existing approach, extreme machine learning (ELM), kernel principal component analysis (KPCA) and kernel extreme machine (KELM) and tuning swarm rapid swarm algorithm (TSRSA) methods are demonstrated. In this research the proposed model is compared with the existing model with various performance parameters.

Keywords: XML data; classification; feature extraction; TIHPOA.

DOI: 10.1504/IJESMS.2023.131788

International Journal of Engineering Systems Modelling and Simulation, 2023 Vol.14 No.3, pp.117 - 124

Received: 19 Jul 2021
Accepted: 16 Nov 2021
Published online: 03 Jul 2023 *

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