Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Speeding up Very Fast Decision Tree with Low Computational Cost
Authors: Jian Sun, Hongyu Jia, Bo Hu, Xiao Huang, Hao Zhang, Hai Wan, Xibin Zhao
IJCAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,275 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,961 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments are conducted using multiple synthetic and real datasets. |
| Researcher Affiliation | Academia | 1 KLISS, BNRist, School of Software, Tsinghua University, China 2 Beijing University of Posts and Telecommunications, China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Online Decision Tree Induction; Function 2 Attempt To Split(l, G, X, δ, τ); Algorithm 3 Online Decision Tree Induction with IMAC; Function 4 Attempt To Split With IMAC(l, G, X, δ, τ, K) |
| Open Source Code | Yes | Our code is available at Git Hub1. 1https://github.com/yearsj/IMAC |
| Open Datasets | Yes | We use large streams consisting of well known real-world and synthetic datasets. Table 1 shows detailed information. Synthetic data (SEA [Street and Kim, 2001], LED [Breiman et al., 1984], AGR [Agrawal et al., 1993], RTG [Domingos and Hulten, 2000], RBF) are all generated using the API proposed by MOA. [...] Covertype. The forest covertype data set [...] KDD99. KDD99 dataset [...] MNIST8M. MNIST8M is the augmentation of original MNIST [Le Cun et al., 1998] database by using pseudorandom deformations and translations [Loosli et al., 2007]. |
| Dataset Splits | No | The paper uses standard datasets but does not explicitly state the train/validation/test dataset splits, specific percentages, or a cross-validation setup. |
| Hardware Specification | Yes | All experiments are conducted on a standard server with 36 cores and 125GB memory. |
| Software Dependencies | No | All algorithms and experiments are implemented on the Massive Online Analysis (MOA) platform [Bifet et al., 2010], which is one of the most popular open-source frameworks for data stream mining. (No version numbers provided for MOA or other software dependencies). |
| Experiment Setup | Yes | VFDT with default parameters (nmin = 200, τ = 0.05, δ = 1e 7), uses the majority class in leaves for classification and information gain as the heuristic measure. Since nmin is 200, to compare with VFDT and OSM at the same level, parameter µ and η in IMAC are both set to 200. |