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arXiv Machine Learning By Yu Yan

JevForest: Path Voting for Budgeted Feature Acquisition

Read the original on arXiv Machine Learning →

JevForest is a feature‑acquisition policy that aggregates path‑dependent proposals from bootstrapped trees, weights them by global training information gain, and uses a shared masked classifier to predict from the acquired values. In online experiments, a four‑question forest on AG News achieved 0.729 accuracy versus 0.667 for a static gain ranking and 0.583 for random ordering, while on TREC the ordering reversed. Offline MiniBooNE tests showed accuracies of 0.845 with ten features and 0.885 with forty features, indicating that Jev’s effectiveness varies with task, predictor, and budget constraints.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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