Extracting decision trees from trained neural networks

By R. Krishnan, G. Sivakumar, Pushpak Bhattacharya. 1999

Subfields: Model Architectures, Safety, Robustness & Interpretability

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Informativeness [5, 44, 21, 25, 26, 45, 30, 32, 34, 35, 37, 38, 41, 46, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 63, 64, 65, 66, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 86, 87, 88, 89, 59, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 1”
A Survey of Methods for Explaining Black Box Models (2018, methodology): “g explanations rather than the logs of previous plays. Table 3. Summary of methods for opening and explaining black boxes with respect to the problem faced. Problem References Model Explanation [20], [50], [9], [39], [14], [29], [114], [87], [32], [94], [5], [19], [37], [115], [6], [70], [28], [65],”
Methods for interpreting and understanding deep neural networks (2017, methodology): “Furthermore, while some methods aim at reaching a comprehensive functional understanding of the model [32,41,8], we will focus here on interpreting the outputs of a DNN, and explaining individual predictions.”
Neural Decision Trees (2017, methodology): “Using an ANN to ﬁlter a dataset prior to learning a DT is explored in Krishnan et al. (1999).”
Hybrid decision tree (2002, methodology): “Krishnan et al. (1999) presented a methodology for extracting decision trees from input data generated from trained neural networks where genetic algorithm was used to query the trained network and a prototype selection mechanism was then used to extract the decision tree.”
Questioning the AI: Informing Design Practices for Explainable AI User Experiences (2020, methodology): “features used by the model (including visualization that shows the weights of features) [41,60,69, 90] How (Global) Decision tree approximation Approximate the model to an interpretable decision-tree [11,47,52] How, Why, Why not, What if Rule extraction Approximate the model to a set of rules, e.g.,”
Explaining prediction models and individual predictions with feature contributions (2014, unknown): “Complex models, such as artificial neural networks [14, 15, 16, 17, 18] and SVM (Support Vector Machines) [19, 20, 10, 21, 22, 23, 24, 25] received most attention, because they are often very successful but non-transparent.”