Robust Machine Learning Models for Imbalanced Dataset Classification

Abstract

The problem of class imbalance in machine learning classification is widely present and difficult across the machine learning area, especially in real-world tasks, including fraud detection, medical diagnosing, network intrusion detection and fault prediction. When this occurs, the minority population is more likely to capture the important occurrences and the conventional machine learning models normally focus on the majority population and give misleading accuracy with poor generalization and high costs of misclassification. This paper is the result of an extensive research into powerful machine learning techniques in the classification of imbalanced datasets. The paper presents a systematic review of theoretical underpinnings of learning imbalance, literature reviews on state-of-the-art methods, such as data, algorithm-level and ensemble based methods, and suggests a convergent system methodology to build a robust classifier. Linear resampling algorithms, cost-effective learning algorithms, hybrid ensemble algorithms, and imbalanced data evaluation metrics are discussed in details. An organized experimental procedure is described to measure robustness when imbalance ratios and various noise levels are changing. Comparative findings indicate that hybrid methods that combine adaptive resampling and cost sensitive loss functions are always better than simpler classifiers based on their F1-score, G-mean, and area under the precision-recall curve. The discussion demonstrates practical trade-offs between model performance, model complexity and interpretability. In the conclusion part, the paper highlights future research directions which include scalable imbalance learning, deep learning adaptations and domain aware evaluation strategies. The paper is an excellent source of information to a researcher and practitioner aiming at finding principled and effective solutions to imbalanced classification problems.