Sukumar Kishanthan

Sukumar Kishanthan, Asela Hevapathige

Class imbalance is a common challenge in machine learning and data mining, often leading to suboptimal performance in classifiers. While deep learning excels in feature extraction, its performance still deteriorates under imbalanced data. In this work, we propose a novel activation function, named OGAB, designed to alleviate class imbalance in deep learning classifiers. OGAB incorporates orthogonality and group-aware bias learning to enhance feature distinguishability in imbalanced scenarios without explicitly requiring label information. Our key insight is that activation functions can be used to introduce strong inductive biases that can address complex data challenges beyond traditional non-linearity. Our work demonstrates that orthogonal transformations can preserve information about minority classes by maintaining feature independence, thereby preventing the dominance of majority classes in the embedding space. Further, the proposed group-aware bias mechanism automatically identifies data clusters and adjusts embeddings to enhance class separability without the need for explicit supervision. Unlike existing approaches that address class imbalance through preprocessing data modifications or post-processing corrections, our proposed approach tackles class imbalance during the training phase at the embedding learning level, enabling direct integration with the learning process. We demonstrate the effectiveness of our solution on both real-world and synthetic imbalanced datasets, showing consistent performance improvements over both traditional and learnable activation functions.

Sukumar Kishanthan, Asela Hevapathige

Despite extensive research spanning several decades, class imbalance is still considered a profound difficulty for both machine learning and deep learning models. While data oversampling is the foremost technique to address this issue, traditional sampling techniques are often decoupled from the training phase of the predictive model, resulting in suboptimal representations. To address this, we propose a novel learning framework that can generate synthetic data instances in a data-driven manner. The proposed framework formulates the oversampling process as a composition of discrete decision criteria, thereby enhancing the representation power of the model's learning process. Extensive experiments on the imbalanced classification task demonstrate the superiority of our framework over state-of-the-art algorithms.

Sukumar Kishanthan, Asela Hevapathige

Class imbalance in machine learning poses a significant challenge, as skewed datasets often hinder performance on minority classes. Traditional oversampling techniques, which are commonly used to alleviate class imbalance, have several drawbacks: they treat features independently, lack similarity-based controls, limit sample diversity, and fail to manage synthetic variety effectively. To overcome these issues, we introduce AxelSMOTE, an innovative agent-based approach that views data instances as autonomous agents engaging in complex interactions. Based on Axelrod's cultural dissemination model, AxelSMOTE implements four key innovations: (1) trait-based feature grouping to preserve correlations; (2) a similarity-based probabilistic exchange mechanism for meaningful interactions; (3) Beta distribution blending for realistic interpolation; and (4) controlled diversity injection to avoid overfitting. Experiments on eight imbalanced datasets demonstrate that AxelSMOTE outperforms state-of-the-art sampling methods while maintaining computational efficiency.