| --- |
| 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](https://huggingface.co/datasets/sh-islam/ciln-bench-cifar10), [ciln-bench-mnist](https://huggingface.co/datasets/sh-islam/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 |
|
|
| ```python |
| 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 |
|
|
| ```bibtex |
| @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. |
|
|
