Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Gossen, Frederik | - |
dc.contributor.author | Steffen, Bernhard | - |
dc.date.accessioned | 2022-03-10T15:02:50Z | - |
dc.date.available | 2022-03-10T15:02:50Z | - |
dc.date.issued | 2021-09-29 | - |
dc.identifier.uri | http://hdl.handle.net/2003/40778 | - |
dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-22635 | - |
dc.description.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. | en |
dc.language.iso | en | de |
dc.relation.ispartofseries | International journal on software tools for technology transfer;Vol. 23. 2021, Art. Nr. 635 | - |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | Random forest | en |
dc.subject | Algebraic decision diagram | en |
dc.subject | Aggregation | en |
dc.subject | Explainability | en |
dc.subject | Interpretability | en |
dc.subject | Running time optimisation | en |
dc.subject | Memory optimisation | en |
dc.subject.ddc | 004 | - |
dc.title | Algebraic aggregation of random forests | en |
dc.title.alternative | towards explainability and rapid evaluation | en |
dc.type | Text | de |
dc.type.publicationtype | article | de |
dc.subject.rswk | Entscheidungsgraph | de |
dc.subject.rswk | Aggregation | de |
dc.subject.rswk | Laufzeit | de |
dc.subject.rswk | Erklärung | de |
dc.subject.rswk | Klassifikator <Informatik> | de |
dc.subject.rswk | Speicher <Informatik> | de |
dc.subject.rswk | Optimierung | de |
dcterms.accessRights | open access | - |
eldorado.secondarypublication | true | de |
eldorado.secondarypublication.primaryidentifier | https://doi.org/10.1007/s10009-021-00635-x | de |
eldorado.secondarypublication.primarycitation | International journal on software tools for technology transfer. Vol. 23. 2021, Art. Nr. 635 | en |
Appears in Collections: | LS 14 Software Engineering |
Files in This Item:
File | Description | Size | Format | |
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Gossen-Steffen2021_Article_AlgebraicAggregationOfRandomFo.pdf | 1.53 MB | Adobe PDF | View/Open |
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