In the wake of the exponential growth in data generation witnessed in recent decades, the binary classification task within data streams presents inherent challenges due to their continuous, real-time flow and dynamic nature. This paper introduces the Balanced Accuracy-based Sliding Window Ensemble (BASWE) algorithm that leverages Balanced Accuracy, sliding windows, and resampling techniques to effectively handle imbalanced classes and concept drifts, ensuring robust performance even as data patterns evolve. In experiments conducted on 40 datasets, comprising 16 real-world and 24 synthetic datasets generated under three configurations-no drift, gradual drift, and sudden drift-and with varying imbalance ratios, BASWE demonstrated superior performance compared to seven other state-of-the-art algorithms in terms of F1 Score and the Kappa statistic.