Robust Optimal Classification Trees against Adversarial Examples
Authors: Daniël Vos, Sicco Verwer8520-8528
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate that the existing heuristics achieve close to optimal scores while ROCT achieves state-of-the-art scores. |
| Researcher Affiliation | Academia | Dani el Vos, Sicco Verwer Delft University of Technology d.a.vos@tudelft.nl, s.e.verwer@tudelft.nl |
| Pseudocode | No | No explicitly labeled pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | ROCT1 uses a novel translation of the problem of fitting robust decision trees into Mixed-Integer Linear Programming (MILP) or Maximum Satisfiability (Max SAT) formulations. 1https://github.com/tudelft-cda-lab/ROCT |
| Open Datasets | Yes | The datasets are summarized in Table 3 and are available on Open ML4. 4http://www.openml.org |
| Dataset Splits | Yes | To this end we select the best value for the maximum depth hyperparameter using 3-fold stratified cross validation on the training set. |
| Hardware Specification | Yes | All of our experiments ran on 15 Intel Xeon CPU cores and 72 GB of RAM total, where each algorithm ran on a single core. |
| Software Dependencies | Yes | solve it using GUROBI2 9. (...) Both algorithms use the Glucose3 4.1 SAT solver. |
| Experiment Setup | Yes | In each run, every algorithm gets 30 minutes to fit. (...) To this end we select the best value for the maximum depth hyperparameter using 3-fold stratified cross validation on the training set. (...) For each dataset we used an 80%-20% train-test split. (...) As the dual of the MILP-based formulations is hard to solve, we focus the solver on the primal problem. (...) In our experiments we choose three ϵ values for each dataset such that their values corresponds to an adversarial accuracy bound that is at 25%-50%-75% of the range. |