Fully-Dynamic Decision Trees
Authors: Marco Bressan, Gabriel Damay, Mauro Sozio
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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 marco.bressan@unimi.it, gabriel.damay@telecom-paris.fr, sozio@telecom-paris.fr |
| 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. |