A Novel Feature Space Augmentation Method to Improve Classification Performance and Evaluation Reliability

Classification tasks in many real-world domains are exacerbated by class imbalance, relatively small sample sizes compared to high dimensionality, and measurement uncertainty. The problem of class imbalance has been extensively studied, and data augmentation methods based on interpolation of minority class instances have been proposed as a viable solution to mitigate imbalance. It remains to be seen whether augmentation can be applied to improve the overall performance while maintaining stability, especially with a limited number of samples. In this paper, we present a novel feature-space augmentation technique that can be applied to high-dimensional data for classification tasks and address these issues. Our method utilizes uniform random sampling and introduces synthetic instances by taking advantage of the local distributions of individual features in the observed instances. The core augmentation algorithm is class-invariant, which opens up an unexplored avenue of simultaneously improving and stabilizing performance by augmenting unlabeled instances. The proposed method is evaluated using a comprehensive performance analysis involving multiple classifiers and metrics. Comparative analysis with existing feature space augmentation methods strongly suggests that the proposed algorithm can result in improved classification performance while also increasing the overall reliability of the performance evaluation.