Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fully-Dynamic Decision Trees
Authors: Marco Bressan, Gabriel Damay, Mauro Sozio
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical results with an extensive experimental evaluation on real-world data, showing the effectiveness of our algorithm. Our contributions can be summarized as follows: We conduct an extensive experimental evaluation on real-world data, evaluating FUDYADT s speed and accuracy against state-of-the-art tools such as EFDT and HAT. |
| Researcher Affiliation | Academia | 1University of Milan, 2Institut Polytechnique de Paris, T el ecom Paris EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 FUDYADT.UPDATE; Algorithm 2 FUDYADT.BUILD |
| Open Source Code | Yes | We implemented FUDYADT in C++ 2. (Footnote 2: https://github.com/GDamay/dynamic-tree) |
| Open Datasets | Yes | Our datasets are shown in Table 1. (Table 1 lists: Electricity, Forest Covertype, INSECTS v1-v5, KDDCUP99, NOAA Weather, Poker) |
| Dataset Splits | No | The paper describes a sequential processing approach and mentions a 'grace period' for baseline parameter tuning, but it does not specify explicit train/validation/test splits with percentages or counts for its own experiments. The concept of a distinct 'validation' set is not explicitly defined for model training or selection. |
| Hardware Specification | Yes | We conducted all experiments on an Ubuntu 20.04.2 LTS server equipped with 144 Intel(R) Xeon(R) Gold 6154 @ 3.00GHz CPUs and 264 GB of RAM. |
| Software Dependencies | No | The paper states: 'We implemented FUDYADT in C++' and 'using the MOA software'. However, it does not provide specific version numbers for C++ compiler, MOA software, or any other libraries used, which are required for full reproducibility. |
| Experiment Setup | Yes | For FUDYADT, we let α = 0, β = 0 k = 1, h ∈ {5, 10}, and we manually set ϵ ∈ [0, 2]. For the SW model we use W ∈ {100, 1000}. The parameters of EFDT and HAT are set to the original values specified by the authors; we only vary the so-called grace period in {100, 500, 1000} to find the value yielding highest F1-score. |