To extend the FMLFS method to handle dynamic feature sets or evolving multi-label datasets in IoT environments, several strategies can be implemented. One approach is to incorporate an adaptive feature selection mechanism that can adjust to changes in the dataset over time. This can involve regularly reevaluating the relevance and redundancy of features based on incoming data and updating the feature rankings accordingly. Additionally, implementing a feedback loop where the model learns from its performance and adapts its feature selection criteria can help in handling dynamic datasets. Techniques such as online learning or incremental feature selection can also be employed to continuously update the feature set as new data arrives. By integrating these adaptive strategies, the FMLFS method can effectively handle evolving datasets in IoT environments.