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Decision Snippet Features

: Welke, Pascal; Alkhoury, Fouad; Bauckhage, Christian; Wrobel, Stefan


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
ICPR 2020, 25th International Conference on Pattern Recognition. Proceedings : 10-15 January 2021, Milan, Italy, Virtual
Piscataway, NJ: IEEE, 2021
ISBN: 978-1-7281-8809-6
ISBN: 978-1-7281-8808-9
International Conference on Pattern Recognition (ICPR) <25, 2021, Online>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
01-S18038CB; ML2R
Deutsche Forschungsgemeinschaft DFG
EXC 2070; 390732324
Fraunhofer IAIS ()
performance evaluation; predictive model; hardware; pattern recognition; decision trees; random forests

Decision trees excel at interpretability of their prediction results. To achieve required prediction accuracies, however, often large ensembles of decision trees random forests are considered, reducing interpretability due to large size. Additionally, their size slows down inference on modern hardware and restricts their applicability in low-memory embedded devices. We introduce Decision Snippet Features, which are obtained from small subtrees that appear frequently in trained random forests. We subsequently show that linear models on top of these features achieve comparable and sometimes even better predictive performance than the original random forest, while reducing the model size by up to two orders of magnitude.