Decision forest: Twenty years of research

By Lior Rokach. Information Fusion, 2016

428 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/24800233be0ee6ab11024d1d42f3328e8cebf228

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In Artificial Intelligence

Citations in the corpus

Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions (2023, background): “Accordingly, models that are ante-hoc explainable, such as DTs [66], are preferred for many applications [67].”
A comparative analysis of gradient boosting algorithms (2019, neutral): “2014; Caruana and Niculescu-Mizil 2006; Rokach 2016).”
Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI (2021, methodology): “We suggest to transform the CG to a Decision Forest (DF) [89] classifier comprising multiple trees derived from semantically enriched counterfactual subgraphs.”
Ensemble learning: A survey (2018, background): “Recently, several comprehensive surveys on ensemble learning have been published in the literature (Gomes, Barddal, Enembreck, & Bifet, 2017; Kulkarni & Sinha, 2013; Rokach, 2016; Wo ź niak, Graña, & Corchado, 2014).”
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary (2018, methodology): “Some clear examples of this behavior are decision trees (Rokach, 2016), and the well-known MapReduce programming model that is used for Big Data applications (Dean & Ghemawat, 2008; Fernández et al.”
A survey on missing data in machine learning (2021, background): “However, decision trees can produce a complex tree that tend to be time consuming but have a low bias [86].”
Born-Again Tree Ensembles (2020, methodology): “For a detailed discussion on this stream of research we refer to Rokach (2016), who discusses the development of ranking-based methods (see, e.g., Prodromidis et al., 1999; Caruana et al., 2004; Banﬁeld et al., 2005; Hu et al., 2007; Partalas et al., 2010; Rokach, 2009; Zhang & Wang, 2009) and…”