Interpretable deep learning-based risk evaluation approach

Cuong Do; Cory Wang

doi:10.1117/12.2583972

20 September 2020 Interpretable deep learning-based risk evaluation approach

Cuong Do, Cory Wang

Proceedings Volume 11543, Artificial Intelligence and Machine Learning in Defense Applications II; 115430S (2020) https://doi.org/10.1117/12.2583972
Event: SPIE Security + Defence, 2020, Online Only

Conference Poster

Abstract

There are two goals of modeling, including interpretation that is to extract information about how the response variables are associated to the input variables, and prediction that is to predict what the responses are going to be. The dilemma is that interpretable algorithms such as linear regression or logistic regression are often not accurate for prediction, while complex algorithms for better prediction are much more accurate but not easy to interpret1. Risk could be in the forms of cyber security risk, credit risk, investment risk, operational risk, etc. In this paper, we propose an interpretable method in evaluating risk using Deep Learning.

Citation Download Citation

Cuong Do and Cory Wang "Interpretable deep learning-based risk evaluation approach", Proc. SPIE 11543, Artificial Intelligence and Machine Learning in Defense Applications II, 115430S (20 September 2020); https://doi.org/10.1117/12.2583972

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