TY  - CONF
N1  - cited By 3; Conference of 2nd Joint International Conference on Emerging Computing Technology and Sports, JICETS 2019 ; Conference Date: 25 November 2019 Through 27 November 2019; Conference Code:161273
N2  - 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.
KW  - Cluster analysis; Computation theory; Decision theory; Lithology; Multivariant analysis; Principal component analysis; Sports; Supervised learning; Well logging
KW  -  Classification approach; Decision rules; Electrofacies; Feature selection methods; Multivariate statistical analysis; Reservoir characterization; Rough set theory (RST); Supervised machine learning
KW  -  Rough set theory
ID  - scholars13014
TI  - Supervised Machine Learning in Electrofacies Classification: A Rough Set Theory Approach
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087451406&doi=10.1088%2f1742-6596%2f1529%2f5%2f052048&partnerID=40&md5=0db566b39713f6ddec0b6f3f4ee21acf
A1  - Hossain, T.M.
A1  - Wataada, J.
A1  - Hermana, M.
A1  - Aziz, I.A.
VL  - 1529
Y1  - 2020///
SN  - 17426588
PB  - Institute of Physics Publishing
ER  -