GANDHI RIFAL
THESIS
Hybrid Smote-Adaboost-Evolutionary Algorithm to Overcome Class Imbalancies and Noisy Attributes
in Well Log Data for Lithology Prediction with K-Nearest Neighbor
ABSTRACT
Lithology prediction using well log data is a critical process in the oil and gas industry to accurately determine geological formations. Various machine learning methods including K-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine have been applied to this task. While these methods offer advantages such as handling multi-class classification, they face significant challenges related to class imbalance and noisy attributes in the dataset. Class imbalance can cause bias toward majority classes, while noisy attributes reduce the model’s ability to detect relevant patterns thereby lowering prediction accuracy. Conventional approaches such as SMOTE (Synthetic Minority Oversampling Technique) to address class imbalance offer generate synthetic data that amplify noisy attributes, increasing the risk of overfitting. Furthermore, boosting methods like AdaBoost strengthen predictions by combining weak models but remain vulnerable to noise in the data. To address these limitations this study proposes the integration of Hybrid SMOTE, AdaBoost, and Evolutionary Algorithm. SMOTE is used to balance class distributions by generating more meaningful synthetic data. The Evolutionary Algorithm is applied for feature selection to minimize noisy attributes, while AdaBoost enhances the model’s robustness against overfitting. The proposed approach was tested on two public datasets, FORCE 2020 and KAGGLE. Experimental results show that this integration significantly improves prediction accuracy, achieving 96.75%, precision 88,83%, recall 84,55%, FI-Score 86,46% on the FORCE 2020 dataset and 76.37%, 70.1 1%, precision 57,06%, recall. 55,05%, F1-Score 55.86% on the KAGGLE dataset, outperforming conventional methods. This study aims to provide an innovative and robust solution to address geological data challenges, improve lithology prediction accuracy, and make substantial contributions to applications in the oil and gas industry.