Authors : Shaik Mahmood-Ur-Rahaman; Soora Sudheer

Volume/Issue : Volume 10 - 2025, Issue 7 - July

Google Scholar : https://tinyurl.com/hkkvfjp2

Scribd : https://tinyurl.com/mrx58ddv

DOI : https://doi.org/10.38124/ijisrt/25jul1197

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Abstract : In recent years, the exponential growth of high-dimensional datasets across fields such as genomics, finance, and cybersecurity has amplified the need for efficient and interpretable machine learning systems. While deep learning models demonstrate remarkable accuracy in pattern recognition tasks, they often lack transparency, posing challenges for trust, accountability, and regulatory compliance. Explainable Artificial Intelligence (XAI) has emerged as a critical research frontier aimed at bridging this interpretability gap. However, most standalone XAI models sacrifice performance for transparency, especially in high-dimensional spaces. This research investigates the efficiency of hybrid XAI models—those that integrate interpretable layers, post-hoc explanation methods, or modular learning structures—with conventional high- performance models to balance accuracy and interpretability. The study adopts a comparative experimental approach using datasets from image recognition and bioinformatics, applying hybrid models such as SHAP-integrated convolutional neural networks (CNNs) and attention-guided recurrent networks. Key performance indicators include classification accuracy, feature importance fidelity, and explanation stability. Statistical tools such as ANOVA and confidence interval analysis are employed to evaluate significance across models. Findings suggest that hybrid models can retain competitive accuracy while offering clearer feature-level insights, thereby enhancing stakeholder trust and model accountability. Furthermore, these models demonstrate potential in uncovering latent patterns often missed by conventional dimensionality reduction techniques. The study underscores the viability of hybrid XAI models in critical decision-making domains, advocating for their broader adoption in real-world high-dimensional data mining tasks (Doshi-Velez & Kim, 2017).

Keywords : Explainable Artificial Intelligence (Xai), High-Dimensional Data, Hybrid Models, Feature Extraction, Pattern Recognition, Deep Learning.

References :

1. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.
2. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
3. Bellman, R. (1961). Adaptive Control Processes: A Guided Tour. Princeton University Press.
4. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
5. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the Conference on Fairness, Accountability, and Transparency, 77–91.
6. Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics, 8(8), 832.
7. Chen, J., Song, L., Wainwright, M. J., & Jordan, M. I. (2021). Learning to explain: An information-theoretic perspective on model interpretation. Journal of Machine Learning Research, 22(1), 1–68.
8. Dignum, V. (2018). Ethics in artificial intelligence: Introduction to the special issue. Ethics and Information Technology, 20, 1–3.
9. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
10. Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018). Explaining explanations: An overview of interpretability of machine learning. IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), 80–89.
11. Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G. Z. (2019). XAI—Explainable artificial intelligence. Science Robotics, 4(37), eaay7120.
12. Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.
13. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
14. Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017). What do we need to build explainable AI systems for the medical domain? arXiv preprint arXiv:1712.09923.
15. Jain, S., & Wallace, B. C. (2019). Attention is not Explanation. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, 3543–3556.
16. Krizhevsky, A. (2009). Learning multiple layers of features from tiny images. Technical Report, University of Toronto.
17. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
18. Lipton, Z. C. (2018). The mythos of model interpretability. Communications of the ACM, 61(10), 36–43.
19. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems (NeurIPS), 30, 4765–4774.
20. Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296.