Sep 2026· Journal of Hillside College of Engineering· 0 citations· 21 references
TL;DR
In this paper, first the imbalanced data are sampled using SMOTE-TOMEK Links sampling which balances the imbalance dataset, then, Ensemble Technique is used for data classification and performance is measured.
Abstract
This century has seen a substantial increase in the value of data. Many decisions are made based on data. More the data, more possibility to get accurate result. But in many cases, available datasets are imbalanced i.e. one class have more data and other class have few data which leads to inaccurate classification while using machine learning. So, this paper focuses to minimize this problem. In this paper, first the imbalanced data are sampled using SMOTE-TOMEK Links sampling which balances the imbalance dataset. Then, Ensemble Technique is used for data classification. In ensemble technique, XGBoost and Random Forest algorithm are used as base classifier and Logistic Regression algorithm is used as meta classifier using Stacking Classifier for final prediction. The performance of this technique is measured using different evaluation metrics, precision, recall, f1-score and ROC (Receiver Operating Characteristics) Curve.
This thesis focus on enhancing the performance of XGBoost for the classification of imbalanced data by performing k-fold cross validation and hyper- parameters tuning.
Amit Rauniyar, Prem Chandra Roy, Anisha Pokhrel et al.· Journal of Hillside College...· 0 citations
Class imbalance is a common problem in machine learning classification tasks and may negatively affect the reliability and predictive performance of classification models. The research object is the comprehensive evaluation of different resampling techniques applied to a publicly available breast cancer dataset consist...
Ogtay Safaraliyev, K. Mammadova· EUREKA Physics and Engineeri...· 0 citations
Class imbalance remains a critical challenge in supervised learning, often biasing classifiers toward majority classes. While resampling techniques like Synthetic Minority Oversampling Technique (SMOTE) are widely used, the combined effect of data balancing and hyperparameter optimization across diverse datasets is rar...
Necati Vardar, Mehmet Fatih Ören· Sakarya University Journal o...· 0 citations
Overall, logistic regression demonstrates strong and good generalization ability, while ensemble methods constitute robust alternatives for binary classification on structured data.
Zahra Benider, H. Bouzahir, Jaafar Idrais· EPJ Web of Conferences· 0 citations
The proposed framework is able to effectively deal with the class imbalance, reduce the prediction bias, improve minority class detection and offer a scalable and interpretable solution for various real world intelligent decision support applications in different domains.
Prabha Ravi· Journal of Intelligent Decis...· 0 citations
This study presents a comprehensive comparative evaluation of three widely used machine learning algorithms: Logistic Regression, Decision Tree, and Random Forest, for classification tasks, and demonstrates that Random Forest achieves superior generalization performance compared to the other models.
Badhur Ammulya, G. Nagalakshmi· International Journal of Lat...· 0 citations
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