Interpretable models from distributed data via merging of decision trees

By Artur Andrzejak, Felix Langner, Silvestre Zabala. 2013

38 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

A Survey on the Explainability of Supervised Machine Learning (2020, methodology): “Here, the parameters are often abstract and disconnected from the real world, which makes it difficult to interpret and explain the results of deep models (Angelov and Soares, 2019). Samek et al. (2017) describe different methods for visualizing and explaining deep learning models like the Sensitivi”
Explainable Artificial Intelligence: a Systematic Review (2020, methodology): “[294] proposed instead a method to efficiently merge a set DTs, each trained independently on distributed data, into a single tree to overcome the lack of interpretability of the distributed models.”
A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment (2017, background): “Studies on distributed and parallel data mining based on cloud computing platforms have achieved abundant favorable achievements [3], [4].”
A Genetic Algorithm for Interpretable Model Extraction from Decision Tree Ensembles (2017, methodology): “When all the nodes from all the trees are converted to their corresponding hyperplane, the different decision spaces can be merged together by calculating their intersection using a sweep line approach discussed in [7].”
Interpretability of machine learning models and representations: an introduction (2016, background): “Furthermore, the detection by experts of anomalies in the model is only possible with interpretable models [10].”
GENESIM: genetic extraction of a single, interpretable model (2016, methodology): “ all the trees are converted to their corresponding set of hyperplanes, the different decision spaces can be merged together by calculating their intersection using a sweep line approach discussed in [8]. In this approach, each hyperplane is projected on a line segment in each dimension. These line ”