GENESIM: genetic extraction of a single, interpretable model
By Gilles Vandewiele, Olivier Janssens, Femke Ongenae, Filip De Turck, Sofie Van Hoecke. 2016
36 citations recorded by the source corpus.
Models obtained by decision tree induction techniques excel in being interpretable.However, they can be prone to overfitting, which results in a low predictive performance. Ensemble techniques are able to achieve a higher accuracy. However, this comes at a cost of losing interpretability of the resulting model. This makes ensemble techniques impractical in applications where decision support, instead of decision making, is crucial. To bridge this gap, we present the GENESIM algorithm that transforms an ensemble of decision trees to a single decision tree with an enhanced predictive performance by using a genetic algorithm. We compared GENESIM to prevalent decision tree induction and ensemble techniques using twelve publicly available data sets. The results show that GENESIM achieves a better predictive performance on most of these data sets than decision tree induction techniques and a predictive performance in the same order of magnitude as the ensemble techniques. Moreover, the resulting model of GENESIM has a very low complexity, making it very interpretable, in contrast to ensemble techniques.
Subfields: Artificial Intelligence, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Xia Hu
- Mengnan Du
- Ninghao Liu
- Osbert Bastani
- Carolyn Kim
- Yashesh D. Dhebar
- Subramanya P. Nageshrao
- Hamsa Bastani
In Molecular Biology
Citations in the corpus
- Toward Interpretable-AI Policies Using Evolutionary Nonlinear Decision Trees for Discrete-Action Systems (2020, methodology): “P is then used to compute the loss function for splitting the data in the DT. Vandewiele et al. [26] attempted to generate interpretable DTs from an ensemble using a genetic algorithm.”
- Interpreting Blackbox Models via Model Extraction (2017, methodology): “Previous model extraction approaches have focused on interpreting specific families of models such as random forests [45, 18, 46], enabling them to leverage domain-specific knowledge about the internal structure of the model.”
- Techniques for interpretable machine learning (2018, background): “An alternative is to apply interpretable model extraction, also referred as mimic learning [36], which may not have to sacrifice the model performance too much.”