Shaow/Feature_Selection_Dataset

Feature Selection Benchmark Datasets (HRLFS)

This repository hosts the 21 benchmark datasets used in our ACM TKDD paper:

Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning Weiliang Zhang, Xiaohan Huang, Yi Du, Ziyue Qiao, Qingqing Long, Zhen Meng, Yuanchun Zhou, Meng Xiao ACM Transactions on Knowledge Discovery from Data (TKDD), 2026

• 📄 Paper code: https://github.com/coco11563/HARLFS

Dataset Summary

The 21 datasets are publicly available and collected from the Feature Selection Benchmark, NCBI Gene Expression Omnibus (GEO), UCI, Kaggle, OpenML, libSVM, etc. They cover three task types — binary classification (C), multi-class classification (MC), and regression (R) — and span diverse fields including biology, finance, image, and synthetic data. Sample sizes range from 253 to 83,733 and feature dimensions from 21 to 20,670.

Dataset	Task	#Samples	#Features	File
SpectF	C	267	44	spectf.hdf
SVMGuide3	C	1,243	21	svmguide3.hdf
German Credit	C	1,001	24	german_credit.hdf
Credit Default	C	30,000	25	credit_default.hdf
SpamBase	C	4,601	57	spam_base.hdf
Megawatt1	C	253	38	megawatt1.hdf
Ionosphere	C	351	34	ionosphere.hdf
Mice-Protein	MC	1,080	77	mice_protein.hdf
Coil-20	MC	1,440	400	coil-20.hdf
MNIST	MC	10,000	784	mnist.hdf
Otto	MC	61,878	93	otto.hdf
Jannis	MC	83,733	54	jannis.hdf
Cao	MC	4,186	13,488	cao.hdf
Han	MC	2,746	20,670	han.hdf
Openml_586	R	1,000	25	openml_586.hdf
Openml_589	R	1,000	25	openml_589.hdf
Openml_607	R	1,000	50	openml_607.hdf
Openml_616	R	500	50	openml_616.hdf
Openml_618	R	1,000	50	openml_618.hdf
Openml_620	R	1,000	25	openml_620.hdf
Openml_637	R	500	50	openml_637.hdf

Data Format

Each dataset is stored as a single HDF5 file with two keys:

• raw_train — training split (80% of samples)
• raw_test — test split (20% of samples)

Each key stores a pandas.DataFrame where the last column is the label and all preceding columns are features.

Usage

Download the files with huggingface_hub and load them with pandas:

import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="Shaow/Feature_Selection_Dataset",
    filename="spam_base.hdf",
    repo_type="dataset",
)

train = pd.read_hdf(path, key="raw_train")
test = pd.read_hdf(path, key="raw_test")

X_train, y_train = train.iloc[:, :-1].to_numpy(), train.iloc[:, -1].to_numpy()
X_test, y_test = test.iloc[:, :-1].to_numpy(), test.iloc[:, -1].to_numpy()

To reproduce the experiments in the paper, place the .hdf files under the ./data directory of the HARLFS repository and run, e.g.:

python HRLFS.py --dataset spam_base

Note: reading these files requires pandas and tables (PyTables): pip install pandas tables.

Licensing

All datasets are redistributed from publicly available sources (Feature Selection Benchmark, NCBI GEO, UCI, Kaggle, OpenML, libSVM). Please refer to the original sources for their respective license terms; this collection is provided for research purposes only.

Citation

If you use these datasets, please cite our paper:

@article{zhang2026comprehend,
  title={Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning},
  author={Zhang, Weiliang and Huang, Xiaohan and Du, Yi and Qiao, Ziyue and Long, Qingqing and Meng, Zhen and Zhou, Yuanchun and Xiao, Meng},
  journal={ACM Transactions on Knowledge Discovery from Data},
  year={2026}
}

Contact

For questions, please contact the corresponding author: Meng Xiao (shaow@cnic.cn).