  Explainable Artificial Intelligence (XAI) models have recently attracted a
great deal of interest from a variety of application sectors. Despite
significant developments in this area, there are still no standardized methods
or approaches for understanding AI model outputs. A systematic and cohesive
framework is also increasingly necessary to incorporate new techniques like
discriminative and generative models to close the gap. This paper contributes
to the discourse on XAI by presenting an empirical evaluation based on a novel
framework: Sampling - Variational Auto Encoder (VAE) - Ensemble Anomaly
Detection (SVEAD). It is a hybrid architecture where VAE combined with ensemble
stacking and SHapley Additive exPlanations are used for imbalanced
classification. The finding reveals that combining ensemble stacking, VAE, and
SHAP can. not only lead to better model performance but also provide an easily
explainable framework. This work has used SHAP combined with Permutation
Importance and Individual Conditional Expectations to create a powerful
interpretability of the model. The finding has an important implication in the
real world, where the need for XAI is paramount to boost confidence in AI
applications.
