Explainable artificial intelligence has gained significant attention, with decision trees playing a key role due to their interpretability. However, incremental decision trees, namely Hoeffding trees (HT), widely used for efficient and transparent data stream processing, suffer from unbounded growth. Existing adaptive methods address this but overlook transparency. We introduce Pruning Hoeffding Trees by Feature Importance (ProeFI), a novel approach that, in a transparent manner, prunes HT to mitigate unbounded growth and enhance adaptability to evolving data. ProeFI employs incremental permutation feature importance and a self-adaptive threshold to dynamically refine its pruning process in response to drifting data distributions. Experimental results show ProeFI achieves comparable performance to state-of-the-art methods while maintaining similar tree complexity. Our method outperforms existing techniques in balancing predictive performance and complexity.