In streaming data environments, drift is observed as changes in the statistical properties of input features. Detection of such phenomena is important because it affects the performance of machine learning models. Traditional drift detection methods often focus on error rates or output distributions, neglecting the root cause: changes in feature importance. This study proposes a drift detection method based on feature ranking in neighboring windows. Feature ranking can be performed using different methods, which will be discussed in the paper. The performance of the drift detector also depends on the classifier used. This work analyzes drifts of different speeds: sudden, gradual, recurrent, and incremental. Experiments on real-world datasets(balanced and imbalanced) show that the proposed method is effective. The paper also provides guidelines for dynamic data environments.