Enhancing Tourism Performance in Oman: A Case Study Using Correlation-Guided Linear Genetic Programming Decision Tree (C-LGPDT)

Created by W.Langdon from gp-bibliography.bib Revision:1.8344

@InProceedings{Al-Jassim:2024:CoDIT,

• author = "Rasha S. {Al Jassim} and Shqran {Al Mansoory} and Karan Jetly and Hilal Ali {Abdullah AlMaqbali} and Muna {Mohammed Albalushi}",
• title = "Enhancing Tourism Performance in Oman: A Case Study Using Correlation-Guided Linear Genetic Programming Decision Tree (C-{LGPDT)}",
• booktitle = "2024 10th International Conference on Control, Decision and Information Technologies (CoDIT)",
• year = "2024",
• pages = "1655--1660",
• month = jul,
• keywords = "genetic algorithms, genetic programming, Accuracy, Evolutionary computation, Machine learning, Feature extraction, Stability analysis, Decision trees, Information technology, Optimisation, Evolutionary Algorithms, Tourism, Decision Tree, Linear Genetic Programming, Classification",
• ISSN = "2576-3555",
• DOI = "doi:10.1109/CoDIT62066.2024.10708185",
• abstract = "This research examines the optimisation of decision tree induction techniques by integrating evolutionary algorithms. It focuses on the Linear Genetic Programming Decision Tree (LGPDT). LGPDT employs a linear program to encode decision trees, achieving an optimal balance between accuracy and interpretability. The study introduces C-LGPDT as an extension of LGPDT, aiming to enhance its efficiency through correlation-based feature selection. This integration reduces dataset dimensionality and eliminates irrelevant or redundant features, resulting in a more accurate and interpretable decision tree model. The performance of C-LGPDT is thoroughly examined, and it is shown that it consistently outperforms older approaches, especially C4.5, and that it is more robust and accurate. A tourism dataset is also used to evaluate the C-LGPDT's performance, with an emphasis on its stability in recall and precision. Results show that C-LGPDT is effective at solving decision tree induction problems, making it a good candidate for machine learning classification tasks.",
• notes = "Also known as \cite{10708185}",

}

Genetic Programming entries for Rasha S Al Jassim Shqran Al mansoory Karan Jetly Hilal Al-maqbali Muna Mohammed Abdul Rahman AL Balushi

Citations