\section{Conclusion and Future Work}
\label{sec:conclusion}
The proposed approach of Sign Entropy regularization to enforce sparsity of coefficients achieved robustness in sign flips while maintaining the model's predictive power. The application of our proposed regularization method, BELIEF, is also shown for XAI where the approach adeptly estimates and discards superpixels with high sign variability for consistent explanation. Further, BELIEF works without the additional step of feature selection as compared to previous work leading to a considerable gain ($\approx$ 10X) in execution time while maintaining the same level of consistency and fidelity as compared to previous state-of-the-art method. Our results are also supported by statistical tests that provide statistical significance for our claims. The proposed Sign Entropy-based regularization is thus applicable to tabular and image data, proving its versatility for general-purpose regression tasks and explainability. While we demonstrated the effectiveness of BELIEF on tabular and image data, future work can explore its applicability to other use cases where stability of coefficients is crucial, such as finance, healthcare, and Natural Language Processing (NLP).

\begin{comment}
The proposed approach of Sign Entropy regularization to enforce sparsity of coefficients was able to achieve robustness to sign flips while maintaining the predictive power of the model. The application of our proposed regularization method, BELIEF, is also shown for XAI where the approach adeptly estimates and discardssuperpixels with high sign variability for consistent explanation. Further, BELIEF works without the additional step of feature selection as compared to previous work leading to a considerable gain ($\approx$ 10X) in execution time while maintaining the same level of consistency and fidelity as compared to previous state-of-the-art method. Our results are also supported by statistical tests that provide statistical significance for our claims. The proposed Sign Entropy-based regularization is thus applicable to tabular and image data, proving its versatility for general purpose regression tasks and explainability.
\end{comment}
