Abstract Interpretation of Decision Tree Ensemble Classifiers
Authors: Francesco Ranzato, Marco Zanella5478-5486
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluation on the MNIST dataset shows that silva provides a precise and efficient tool which advances the current state of the art in tree ensembles verification. Our experiments were run on a AMD Ryzen 7 1700X 3.0GHz CPU. |
| Researcher Affiliation | Academia | Francesco Ranzato, Marco Zanella Dipartimento di Matematica, University of Padova, Italy {ranzato, mzanella}@math.unipd.it |
| Pseudocode | Yes | Algorithm 1 describes in pseudocode our stability verification methodology. |
| Open Source Code | Yes | We implemented Algorithm 1 in a tool called silva whose source code in C (about 5K LOC) is available on Git Hub (Ranzato and Zanella 2019). |
| Open Datasets | Yes | Our experimental evaluation on the MNIST dataset... The standard training set of MNIST consists of 60000 samples, while its test set T includes the remaining 10000 samples. We also used silva on RFs and GBDTs trained on the Sensorless dataset from the UCI ML Repository |
| Dataset Splits | No | The paper specifies training and test sets for MNIST and Sensorless but does not explicitly mention a validation set or provide details on how validation was performed. |
| Hardware Specification | Yes | Our experiments were run on a AMD Ryzen 7 1700X 3.0GHz CPU. |
| Software Dependencies | No | RFs have been trained by scikit-learn while Cat Boost has been used for GBDTs. We implemented Algorithm 1 in a tool called silva whose source code in C (about 5K LOC) is available on Git Hub (Ranzato and Zanella 2019). The paper mentions software used (scikit-learn, CatBoost, C) but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | Table 1 shows the accuracy and stability percentages and the total verification time on the whole test set of MNIST for different random forest classifiers trained by combining 4 parameters: number B of decision trees, maximum tree depth d, training criterion (Gini and entropy) and voting scheme (max and average). We considered the standard perturbation P ,ϵ (Carlini and Wagner 2017) with ϵ = 1. |