relation: https://khub.utp.edu.my/scholars/13014/
title: Supervised Machine Learning in Electrofacies Classification: A Rough Set Theory Approach
creator: Hossain, T.M.
creator: Wataada, J.
creator: Hermana, M.
creator: Aziz, I.A.
description: Electrofacies were initially introduced for defining a set of recorded log responses in order to characterize a bed and permitted it to be distinguished from the other rock units as an improvement to the traditional use of well logs. Grouping a formation into electrofacies can be used in lithology prediction, reservoir characterization and discrimination. Usually Multivariate statistical analyses, such as principal component analysis 'PCA' and cluster analysis are used for this purpose. In this study Extra Tree Classifier (ETC) based feature selection method is used to select the important attributes and three distinctive electrofacies were extracted from the dendrogram plot using the selected attributes. Finally, we proposed a rough set theory (RST) based white box classification approach to extract the pattern of the electrofacies in the form of decision rules which will allow the geosciences researchers to correlate the electrofacieses with the lithofacies from the extracted rough set (RS) rules. © 2020 IOP Publishing Ltd. All rights reserved.
publisher: Institute of Physics Publishing
date: 2020
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Hossain, T.M. and Wataada, J. and Hermana, M. and Aziz, I.A.  (2020) Supervised Machine Learning in Electrofacies Classification: A Rough Set Theory Approach.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087451406&doi=10.1088%2f1742-6596%2f1529%2f5%2f052048&partnerID=40&md5=0db566b39713f6ddec0b6f3f4ee21acf
relation: 10.1088/1742-6596/1529/5/052048
identifier: 10.1088/1742-6596/1529/5/052048