eprintid: 19007
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/90/07
datestamp: 2024-06-04 14:11:27
lastmod: 2024-06-04 14:11:27
status_changed: 2024-06-04 14:04:37
type: book
metadata_visibility: show
creators_name: Kit, J.L.O.W.
creators_name: Asirvadam, V.S.
creators_name: Hassan, M.F.B.
title: Enterprise knowledge graphs using ensemble learning and data management
ispublished: pub
note: cited By 0
abstract: Ensemble model is made of a set of models that integrate various type supervised for form classifier to increase or boast prediction consistency. This chapter introduced improved algorithm framework for supervised learning which takes the best three classifiers out of six and combine to produce enhanced ensemble model using uniform voting approach. The proposed technique is tested on PIMA Indian Diabetes dataset and showed superior performance compared to classification tree-based extended techniques (e.g., Random Forest and AdaBoost). The new structured formulated ensemble framework introduced also tend to be invariant to size of fold during validation process (k-fold validation). © The Institution of Engineering and Technology 2023.
date: 2023
publisher: Institution of Engineering and Technology
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181627137&partnerID=40&md5=b7a38ffbbf95c9d35942e05888956f35
full_text_status: none
publication: Explainable Artificial Intelligence (XAI): Concepts, enabling tools, technologies and applications
pagerange: 227-238
refereed: TRUE
isbn: 9781839536960; 9781839536953
citation:   Kit, J.L.O.W. and Asirvadam, V.S. and Hassan, M.F.B.  (2023) Enterprise knowledge graphs using ensemble learning and data management.     Institution of Engineering and Technology, pp. 227-238.  ISBN 9781839536960; 9781839536953