Pith Number

pith:ZLZTKUJA

pith:2026:ZLZTKUJAYXPUKWIPQV26DXQGTK

not attested not anchored not stored refs resolved

A Systematic Evaluation of Imbalance Handling Methods in Biomedical Binary Classification

Jiandong Chen, Ju Sun, Le Peng, Lingjie Su, Rui Zhang, Yash Travadi

Imbalance handling boosts complex models on unstructured biomedical data but harms simple ones.

arxiv:2605.14147 v1 · 2026-05-13 · cs.LG

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{ZLZTKUJAYXPUKWIPQV26DXQGTK}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

clear benefits were observed for more complex models and unstructured data: (a) ROS and RW consistently enhanced the performance of powerful models; (b) direct F1-score optimization demonstrated utility primarily for unstructured text and image data; and (c) RUS and SMOTE consistently degraded performance and are therefore not recommended.

C2weakest assumption

That the three chosen public datasets and the selected model architectures sufficiently represent the broader space of biomedical binary classification problems so that the observed patterns generalize.

C3one line summary

Random oversampling and re-weighting boost complex models on unstructured biomedical data, but undersampling and SMOTE degrade results and simple models on tabular data see no benefit.

References

3 extracted · 3 resolved · 1 Pith anchors

[1] Aftab, J. et al. Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture. Sci. Rep. 15, 8 2025 · doi:10.1145/1273496.1273614

[2] Peng, L., Travadi, Y., He, C., Cui, Y. & Sun, J. Exact Reformulation and Optimization for Direct Metric Optimization in Binary Imbalanced Classification. Preprint at https://doi.org/10.48550/arXiv.250 2025 · doi:10.48550/arxiv.2507.15240

[3] MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare 2015 · doi:10.18653/v1/2020.findings-emnlp.187

Receipt and verification

First computed	2026-05-17T23:39:11.617459Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

caf3355120c5df45590f8575e1de069a84adc372ca6e45c6f0b9408aa61771b3

Aliases

arxiv: 2605.14147 · arxiv_version: 2605.14147v1 · doi: 10.48550/arxiv.2605.14147 · pith_short_12: ZLZTKUJAYXPU · pith_short_16: ZLZTKUJAYXPUKWIP · pith_short_8: ZLZTKUJA

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZLZTKUJAYXPUKWIPQV26DXQGTK \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: caf3355120c5df45590f8575e1de069a84adc372ca6e45c6f0b9408aa61771b3

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "eaeacbea86cb77724acb8d2b0c3ae14d722b91944782a9fa53bd7189b76c53f6",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T21:57:38Z",
    "title_canon_sha256": "a44340b3470611b140eb2bbba369beab5a7aae9958673643771f9884001ed26d"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14147",
    "kind": "arxiv",
    "version": 1
  }
}