Evolving decision trees using oracle guides

By Ulf Johansson, Lars Niklasson. 2009

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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, background): “ons 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], [22], [61]”
Methods and Models for Interpretable Linear Classification (2014, background): “Other novel approaches to interpretable classification include: oracle-coaching [44], which uses a highly accurate black-box model to guide the training of a less accurate transparent model; and prototype selection [7], which extracts a small set of representative samples that can illustrate the wor”
Comprehensible classification models: a position paper (2014, background): “to be particularly strong in medical applications [19], [39], [46], [59], [69], where lives are at stake.”
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.,”
A Survey of Evolutionary Algorithms for Decision-Tree Induction (2012, methodology): “This approach or minor variations of it are found in [39], [60], [65], [66], and [74].”
Accurate and interpretable regression trees using oracle coaching (2014, unknown): “…as training data for the transparent model, possibly in conjunction with the original training data The term oracle coaching comes from the underlying idea of viewing the stronger opaque model as an oracle, whose predicted values are regarded as true values by the transparent model during training.”