Authors: Gossen, Frederik
Steffen, Bernhard
Title: Algebraic aggregation of random forests
Other Titles: towards explainability and rapid evaluation
Language (ISO): en
Abstract: Random Forests are one of the most popular classifiers in machine learning. The larger they are, the more precise the outcome of their predictions. However, this comes at a cost: it is increasingly difficult to understand why a Random Forest made a specific choice, and its running time for classification grows linearly with the size (number of trees). In this paper, we propose a method to aggregate large Random Forests into a single, semantically equivalent decision diagram which has the following two effects: (1) minimal, sufficient explanations for Random Forest-based classifications can be obtained by means of a simple three step reduction, and (2) the running time is radically improved. In fact, our experiments on various popular datasets show speed-ups of several orders of magnitude, while, at the same time, also significantly reducing the size of the required data structure.
Subject Headings: Random forest
Algebraic decision diagram
Running time optimisation
Memory optimisation
Subject Headings (RSWK): Entscheidungsgraph
Klassifikator <Informatik>
Speicher <Informatik>
Issue Date: 2021-09-29
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Appears in Collections:LS 14 Software Engineering

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