Weighted Linear Discriminant Analysis: An Effective Feature Extraction Method for Multi-Class Imbalanced Datasets

In high-dimensional machine learning tasks, supervised feature extraction is essential for improving model performance, with Linear Discriminant Analysis (LDA) being a common approach. However, LDA tends to deliver suboptimal performance when dealing with class imbalance. To address this issue, we propose a novel feature extraction model, Weighted Linear Discriminant Analysis (WLDA), which integrates cost-sensitive techniques into the traditional LDA framework. By assigning weights inversely proportional to class sample sizes, WLDA achieves effective feature extraction under imbalanced sample conditions. We introduce an efficient solution algorithm for the proposed model and provide a thorough complexity analysis. Experimental results demonstrate the superior performance of WLDA in handling imbalanced datasets, confirming its potential as a robust tool for high-dimensional data scenarios. Overall, WLDA not only improves feature extraction for imbalanced datasets but also enhances classification accuracy across diverse applications.