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Journal of E-Technology

Using Formal Concept Analysis to Explain Black Box Deep Learning Classification Models
Amit Sangroya, Anantaram C, Mrinal Rawat, Mouli Rastogi
TCS Innovation Labs India
Abstract: Recently many machine learning based AI systems have been designed as black boxes. These are the systems that hide the internal logic from the users. Lack of transparency in decision making limits their use in various real world applications. In this paper, we propose a framework that utilizes formal concept analysis to explain AI models. We use classification analysis to study abnormalities in the data which is further used to explain the outcome of machine learning model. The ML method used to demonstrate the ideas is two class classification problem. We validate the proposed framework using a real world machine learning task: diabetes prediction. Our results show that using a formal concept analysis approach can result in better explanations.
Keywords: AI, AI Models, Machine Learning, Formal Concept Analysis Using Formal Concept Analysis to Explain Black Box Deep Learning Classification Models
DOI:https://doi.org/10.6025/jet/2020/11/1/23-31
Full_Text   PDF 717 KB   Download:   582  times
References:

[1] Ganter, B., and Wille, R. (1999). Formal Concept Analysis, Mathematical Foundations. Berlin,Heidelberg,New York: Springer,
1999.
[2] Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., and Elhadad, N. (2015). Intelligible models for healthcare: Predicting
pneumonia risk and hospital 30-day readmission, in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, ser. KDD ’15. New York, NY, USA: ACM, 2015, pp. 1721–1730. [Online]. Available: http://
doi.acm.org/10.1145/2783258.2788613
[3] Letham, B., Rudin, C., McCormick, T. H., and Madigan, D. (2013). An interpretable stroke prediction model using rules and
bayesian analysis, In: Proceedings of the 17th AAAI Conference on Late-Breaking Developments in the Field of Artificial Intelligence,
ser. AAAIWS’13-17. AAAI Press, 2013, p 65–67. [Online]. Available: http://dl.acm.org/citation.cfm?id=2908286.2908308
[4] Ustun, B., and Rudin, C. (2016). Supersparse linear integer models for optimized medical scoring systems, Mach. Learn., 102(3),
p 349–391, Mar. 2016. [Online]. Available: https://doi.org/10.1007/s10994-015-5528-6
[5] Karpathy, A., and Fei-Fei, L. (2017). Deep visual-semantic alignments for generating image descriptions, IEEE Trans. Pattern
Anal. Mach. Intell., 39 (4), p 664–676, Apr. 2017. [Online]. Available: https://doi.org/10.1109/TPAMI.2016.2598339
[6] Xu, K., Ba, J. L., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R. S., and Bengio, Y. (2015). Show, attend and tell:
Neural image caption generation with visual attention, In: Proceedings of the 32Nd International Conference on International
Conference on Machine Learning - Volume 37, ser. ICML’15. JMLR.org, 2015, pp. 2048–2057. [Online]. Available: http://dl.acm.org/
citation.cfm?id=3045118.3045336
[7] Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., and M¨uller, K.-R. (2010). How to explain individual
classification decisions, J. Mach. Learn. Res., 11, p1803–1831, August 2010. [Online]. Available: http://dl.acm.org/
citation.cfm?id=1756006.1859912
[8] Craven, M. W., and Shavlik, J. W. (1995). Extracting tree-structured representations of trained networks, In: Proceedings of the
8th International Conference on Neural Information Processing Systems, ser. NIPS’95. Cambridge, MA, USA: MIT Press, 1995, p
24–30. [Online]. Available: http://dl.acm.org/citation.cfm?id=2998828.2998832.
9] Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). why should I trust you?: Explaining the predictions of any classifier, in
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA,
USA, August 13-17, 2016, 2016, pp. 1135–1144. [Online]. Available: https://doi.org/10.1145/2939672.2939778
[10] Lundberg, S. M., and Lee, S.-I. (2017). A unified approach to interpreting model predictions, In: Proceedings of the 31st
International Conference on Neural Information Processing Systems, ser. NIPS’17. USA: Curran Associates Inc., 2017, p 4768–
4777. [Online]. Available: http://dl.acm.org/citation.cfm?id=3295222.3295230
p 1543–1564, October 2001. [Online]. Available: https://doi.org/10.1023/A:1012435612567
[12] Poelmans, J., Ignatov, D. I., Kuznetsov, S. O., and Dedene, G. (2013). Review: Formal concept analysis in knowledge processing:
A survey on applications, Expert Syst. Appl., 40 (16), p 6538–6560, Nov. 2013. [Online]. Available: http://dx.doi.org/10.1016/
j.eswa.2013.05.009


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