Knowledge Discovery Via Multiple Models

By Pedro M. Domingos. 1998

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, positive): “The author from [119] poses the idea of training a single albeit less complex model from a set of random samples from the data (ideally following the real data distribution) labeled by the ensemble model.”
A Survey of Methods for Explaining Black Box Models (2018, methodology): “ Expl. DT NN TAB X X X DecText [9] Boz 2002 Model Expl. DT NN TAB X X X GPDT [39] Johansson et al. 2009 Model Expl. DT NN TAB X X X X Tree Metrics [14] Chipman et al. 1998 Model Expl. DT TE TAB X CCM [23] Domingos et al. 1998 Model Expl. DT TE TAB X X X - [29] Gibbons et al. 2013 Model Expl. DT TE T”
Counterfactual Explanations for Machine Learning: A Review (2020, background): “Various papers have developed techniques to explain neural networks and tree ensembles using single decision tree [25, 70, 34] and rule sets [28, 13].”
Optimal Action Extraction for Random Forests and Boosted Trees (2015, methodology): “For example, in order to leverage the interpretability of one single decision tree and the superior accuracy performance of random forest, Domin-gos [11] proposed to rebuild a decision tree based on the randomly generated examples from the random forest.”
Ensemble learning: A survey (2018, methodology): “In CMM, a new model is trained by learning the data partitioning implicitly from the ensemble using the generated data (Domingos, 1998).”
A Case-Based Explanation System for Black-Box Systems (2005, background): “He found that it retained just 60% of the gains [ 4 ].”
Interpretable inverse design of particle spectral emissivity using machine learning (2020, methodology): “We correct this reduced interpretability and solve the inverse problem by employing a Combined Multiple Models (CMM) method 19 .”
Benchmarking and survey of explanation methods for black box models (2021, methodology): “CMM, Combined Multiple Model procedure [41], is a specific global post-hoc explanation method for tree ensembles.”
Confident Interpretation of Bayesian Decision Tree Ensembles for Clinical Applications (2007, methodology): “For interpretation of DT ensembles, two approaches have been suggested [9], [10].”
Accurate and interpretable regression trees using oracle coaching (2014, methodology): “, [1, 2].”
A Perspective View and Survey of Meta-Learning (2002, background): “Domingos (1997, 1998) shows empirical evidence supporting the claim that a meta-learner can improve the accuracy of base-learners while retaining comprehensibility.”
Open the Black Box Data-Driven Explanation of Black Box Decision Systems (2018, methodology): “…experimental validation of the local-first approach to global explanations, we compared it with two baselines: pure global classification [8, 11], i.e., learning a decision tree on the whole training dataset, and the collection of all local explanations obtained applying the local…”
Types of Cost in Inductive Concept Learning (2002, methodology): “When an induced model is used to gain understanding of the underlying process that generated the data, it is important that the model should be stable (Turney, 1995c; Domingos, 1998).”
Seeing the Forest Through the Trees (2007, unknown): “Domingos [8] proposed Combined Multiple Models (CMM).”