Code, Data, and Experimental Results for "Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods"

This dataset contains (1) all code needed to reproduce our results, (2) the 20 data sets on which we report results, and (3) all experiment results, related to the paper: "Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods" as published in TMLR, see: https://openreview.net/forum?id=lscC4PZUE4.<br>The code is written in Python 3.12, a README inside the zipped code folder provides more details on setting up and running the code.<br>In the zipped data sets folder, we provide a README with more information on our preprocessing steps and links to the orginal sources from which we retrieved the data sets.<br>Each experiment is outputted to a JSON file. We include both the results as reported in the paper (the best-found hyperparameter setting) and all experiments related to other hyperparameter settings. The JSON files are organized in zipped folders per experiment type. See the README for further details.

本数据集包含以下三部分内容：(1) 用于复现本文研究结果的全部代码；(2) 本文报告实验结果所依托的20个数据集；(3) 所有与该论文相关的实验结果。本数据集关联发表于TMLR的论文《Boosting Revisited：基准测试与改进基于线性规划（Linear Programming，LP）的集成学习方法》，详情可参阅：https://openreview.net/forum?id=lscC4PZUE4。
该代码基于Python 3.12编写，压缩代码包内附带的README文件提供了代码环境配置与运行的详细说明。
在压缩的数据集文件夹中，我们同样附带了README文件，其中详细说明了数据预处理步骤，并给出了获取原始数据集的来源链接。
每项实验的结果都会输出为JSON文件。我们不仅提供了论文中报告的结果（即寻找到的最佳超参数配置对应的实验结果），还包含了所有其他超参数配置相关的全部实验结果。这些JSON文件按照实验类型分别打包在不同的压缩文件夹中，更多细节请参阅配套的README文件。