Impact of Feature Encoding on Malware Classification Explainability

Autor:	Manai, Elyes, Mejri, Mohamed, Fattahi, Jaouhar
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: This paper is accpeted in the 15th Edition of INTERNATIONAL CONFERENCE on Electronics, Computers and Artificial Intelligence, BUCHAREST, ROMANIA (ECAI 2023)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.05614 Zobrazit plný text záznamu View this record from Arxiv