{
  "abstract": "Choosing which information to observe is central to prediction under limited observation budgets. We study JevForest, a feature acquisition policy that aggregates path-dependent proposals from bootstrapped trees, weights them by global training information gain, and predicts from the acquired values with a shared masked classifier. An online implementation queries Jev for semantic answers selected by this policy. On small balanced held-out samples, four-question forest acquisition achieves accuracy $0.729$ on AG News ($n=48$), compared with $0.667$ for a static gain ranking and $0.583$ for random ordering. On TREC ($n=24$), the ordering reverses: forest accuracy is $0.667$, compared with $0.750$ and $0.833$. Asking all eight questions in one batch yields higher accuracy at lower measured cost and latency than four sequential forest queries; direct Jev classification matches the batch accuracy while costing less. Offline MiniBooNE experiments yield accuracy $0.845\\pm0.010$ at ten features and $0.885\\pm0.008$ at forty features over three jointly varying data and forest seeds (mean $\\pm$ sample standard deviation). A companion Newton boosting implementation provides preliminary full-feature synthetic results. These exploratory findings establish a working Jev acquisition workflow but do not support a general advantage for path voting: its value depends on the task, predictor, and the distinction between question budgets and actual query costs.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.10615",
  "author_total": 1,
  "authors": [
    {
      "name": "Yu Yan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cc8taqq55qh9g.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.10615",
  "fulltext_url": null,
  "id": "cc8taqq55qh9g",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.10615",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Machine Learning and Algorithms"
    ]
  ],
  "openalex": "W7221108764",
  "pdf_url": "https://arxiv.org/pdf/2610.10615",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cc8taqq55qh9g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:03Z",
      "native_id": "2610.10615",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.10615"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "JevForest: Path Voting for Budgeted Feature Acquisition",
  "title_text": "JevForest: Path Voting for Budgeted Feature Acquisition",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:03Z",
  "url": "https://research.draconitic.com/paper/cc8taqq55qh9g",
  "venue": "arXiv",
  "versions": []
}