Optimal Action Extraction for Random Forests and Boosted Trees

By Zhicheng Cui, Yujie He, Yixin Chen. 2015

129 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking (2017, background): “In [6], it has already been proven that a problem similar to the one we define above is NP-hard as it reduces to DNF-MAXSAT.”
Making Tree Ensembles Interpretable (2016, background): “These tree ensembles are collectively referred to as Additive Tree Models (ATMs) (Cui et al., 2015).”
Mathematical optimization in classification and regression trees (2021, background): “The first research avenue we touch on consists of expanding the family of criteria under consideration: on the top of accuracy and sparsity, there may be other important requirements on T , such as fairness, to ensure that T protects sensitive groups (Romei and Ruggieri 2014), or explainability, to ”
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, methodology): “[150] explore ℓ1,ℓ2 norm as well as constant cost if specific actions are undertaken; Karimi et al. [92, 93] minimize the ℓ2 norm between �F and the action � assignment (i.e., ||�||2); and Cui et al. [48] explore combinations of ℓ0,ℓ1,ℓ2 norms over a user-specified cost matrix. Encoding individual-d”
Interpretable Neural Networks for Predicting Mortality Risk using Multi-modal Electronic Health Records (2019, unknown): “…to support clinical interpretability include building a single tree with multivariate decision nodes [7], extracting an optimal tree with a minimum performance cost [8], and an indirect method that offers recommendations for transforming true negative instances into positively predicted ones [9].”
Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable (2016, methodology): “ the sensitivity of the method to different prototype-ﬁnding procedures and demonstrating it on higher-dimensional data. 1 Introduction Ensembles of decision trees, also known as additive tree models [1], have been shown to perform well across a variety of metrics and problems [2]. These models incl”
A Survey on the Explainability of Supervised Machine Learning (2020, background): “This approach attempts to find a feature vector so that the desired output is achieved at a minimum cost (Cui et al., 2015).”