TY  - JOUR
TI  - Performance analysis of set partitioning formulations on the rule extraction from random forests
AB  - Random Forests is a widely used machine learning algorithm for classification and regression problems from different domains. Although they are generally accurate, their interpretability is low compared to their building blocks: single decision trees. Using the fact that each member of a Random Forest is a decision tree, we propose different set partitioning formulations to extract interpretable if-then rules from Random Forests. Our experiments on well-known classification and regression datasets show that the original set partitioning model formulation significantly reduces the number of rules while keeping the accuracy at acceptable levels. We also propose a modification to the problem's objective function, which aims to reduce the number of extracted rules further. We observe a further reduction in the number of extracted rules while the accuracy values stay nearly the same. Although the set partitioning problem is NP-hard, we obtain optimal results for most datasets within twenty minutes.
AU  - Edali, Mert
DO  - 10.5505/pajes.2020.05926
PY  - 2021
JO  - Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi
VL  - 27
IS  - 4
SN  - 2147-5881
SP  - 513
EP  - 519
DB  - TRDizin
UR  - http://search/yayin/detay/456255
ER  -