Datasets:

sh-islam
/

ciln-bench-adult

license: mit
task_categories:
  - tabular-classification
language:
  - en
tags:
  - label-noise
  - instance-dependent-noise
  - benchmark
  - corruption
  - tabular
  - adult
  - reproducibility
size_categories:
  - 100K<n<1M
pretty_name: 'CILN-Bench: Adult'

CILN-Bench: Adult

Instance-dependent label noise benchmarks built from controlled tabular corruptions.

This dataset is the Adult component of CILN-Bench. We corrupt the UCI Adult Income noisy-label-train (NLT) split with 5 standard tabular corruptions (Jenga family — 3 missingness mechanisms + 2 value perturbations, 3 severities each → 15 settings), then let a 5-voter pool classify the corrupted rows. The resulting soft labels are released as a noisy-label benchmark.

Code, examples, and reproducibility tests: https://github.com/sh-islam/ciln-bench
Companion datasets: ciln-bench-cifar10, ciln-bench-mnist

Settings released

5 corruption types × 3 severities = 15 settings.

Family	Corruptions
Missingness	missing_mcar, missing_mar, missing_mnar
Value perturbation	gaussian_noise, scaling

Severity controls the corrupted-row fraction (sev 1 → 5%, sev 3 → 25%, sev 5 → 50%). For missing_mar, corruption probability depends on the observed sex attribute; for missing_mnar, it depends on the values being corrupted.

Noise rate ranges from 14.7% to 26.3% across the 15 settings.

Voter pool

5 voters: XGBoost, CatBoost, RTDL-MLP, FT-Transformer, TabPFN.

Repository layout

settings/
├── gaussian_noise_sev1/
│   ├── noisy_label_train/
│   │   ├── adult_corrupted.parquet   # corrupted feature rows
│   │   ├── labels.npy                # (N,) int — ground-truth income label
│   │   ├── softmax_xgboost_dummyna.npy
│   │   ├── softmax_catboost.npy
│   │   ├── softmax_mlp.npy
│   │   ├── softmax_ft_transformer.npy
│   │   ├── softmax_tabpfn.npy
│   │   ├── avg_softmax.npy
│   │   ├── manifest.json
│   │   └── params.jsonl
│   └── noisy_label_valid/
│       └── ... (same structure)
└── ... (15 settings total)

How to load

import numpy as np
import pandas as pd
from huggingface_hub import snapshot_download

local = snapshot_download(
    repo_id="sh-islam/ciln-bench-adult",
    repo_type="dataset",
    allow_patterns=["settings/missing_mar_sev3/noisy_label_train/*"],
)

features = pd.read_parquet(f"{local}/settings/missing_mar_sev3/noisy_label_train/adult_corrupted.parquet")
labels   = np.load(f"{local}/settings/missing_mar_sev3/noisy_label_train/labels.npy")
print(features.shape, labels.shape)

allow_patterns is a filter that limits which files get downloaded. Pass a glob (or a list of globs) and only matching files come down. Omit it to download the full dataset.

Citation

@inproceedings{cilnbench2027,
  title  = {CILN-Bench: A Benchmark for Corruption-Induced Label Noise},
  author = {Islam, Shadman and Kristiadi, Agustinus and Milani, Mostafa},
  booktitle = {ICDE},
  year   = {2027}
}

License

MIT.