Genetic Programming Based Automated Machine Learning in Classifying ESG Performances
Created by W.Langdon from
gp-bibliography.bib Revision:1.8344
- @Article{Rahman:2024:ACCESS,
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author = "Abdullah {Sani Abd Rahman} and Suraya Masrom and
Rahayu Abdul Rahman and Roslina Ibrahim and
Abdul Rehman Gilal",
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title = "Genetic Programming Based Automated Machine Learning
in Classifying {ESG} Performances",
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journal = "IEEE Access",
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year = "2024",
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volume = "12",
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pages = "59612--59629",
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keywords = "genetic algorithms, genetic programming, Machine
learning, Statistics, Sociology, Machine learning
algorithms, Task analysis, Optimisation, Evolutionary
computation, Business intelligence, Classification
algorithms, Evolutionary computing, classification",
-
ISSN = "2169-3536",
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DOI = "
doi:10.1109/ACCESS.2024.3393511",
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abstract = "AutoML offers significant benefits in solving
real-life problems because it accelerates the
development of machine learning models. In contexts
involving real scenarios like analysing companies'
environmental, social and governance (ESG), where the
dataset presents some challenges, AutoML is anticipated
as a promising solution to address these complexities.
Although researchers have shown significant interest in
exploring Genetic Programming (GP) in AutoML for
handling complex datasets, a critical issue that
remains unresolved is the comprehensive understanding
of GP hyper-parameters that influence machine learning
performance. While GP-based AutoML excels in automating
many aspects of the modelling, there has been a
scarcity of research that provides insight into the
significance of individual features and GP population
size within the models of GP-based AutoML. This paper
presents a comprehensive analysis of the models'
performance evaluation from multiple facets, including
feature selection, GP population sizes, and different
machine learning algorithms. Furthermore, this study
provides insights into the association between Pearson
correlations, machine learning performance, and the
importance of machine learning features. The findings
demonstrate that incorporating all the determinants as
features in GP-based AutoML or relying solely on firm
characteristics led to superior performance with an
excellent trade-off between True Positive Rate and
False Positive Rate. Thus, higher accuracy results
exceeding 0.9 of Area Under the Curve (AUC) are
presented by the proposed models. The novelty of this
study lies in its empirical evaluation of different
approaches to GP-based AutoML implementation. These
findings provide alternative solutions for business
investors to identify companies with strong
sustainability practices.",
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notes = "Also known as \cite{10508368}
Faculty of Sciences and Information Technology,
Universiti Teknologi PETRONAS, Seri Iskandar, Perak,
Malaysia",
- }
Genetic Programming entries for
Abdullah Sani B Abd Rahman
Suraya Masrom
Rahayu Abdul Rahman
Roslina Ibrahim
Abdul Rehman Gilal
Citations