This paper investigates the impact of feature encoding techniques on the
explainability of XAI (Explainable Artificial Intelligence) algorithms. Using a
malware classification dataset, we trained an XGBoost model and compared the
performance of two feature encoding methods: Label Encoding (LE) and One Hot
Encoding (OHE). Our findings reveal a marginal performance loss when using OHE
instead of LE. However, the more detailed explanations provided by OHE
compensated for this loss. We observed that OHE enables deeper exploration of
details in both global and local contexts, facilitating more comprehensive
answers. Additionally, we observed that using OHE resulted in smaller
explanation files and reduced analysis time for human analysts. These findings
emphasize the significance of considering feature encoding techniques in XAI
research and suggest potential for further exploration by incorporating
additional encoding methods and innovative visualization approaches.

研究探究特征编码技术对可解释人工智能算法的影响，发现在恶意软件分类数据集中，使用 One Hot Encoding（OHE）方法相对于使用 Label Encoding（LE）方法，虽然性能略差，但它提供了更详细的解释，深入探索全局和局部上下文的细节，方便提供更全面的答案。此外，使用 OHE 的说明文件更小，分析人员的分析时间也更少。