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Data Collection and Labeling Techniques for Machine Learning

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arxiv 2407.12793 v1 pith:BKAIFKEI submitted 2024-06-19 cs.DB cs.AIcs.LG

Qianyu Huang , Tongfang Zhao This is my paper · ORCID
classification cs.DBcs.AIcs.LG
keywords datacollectionlabelinglearningmachineapplicationstechniquesbecome
verification ladder T0 review T1 audit T2 compute T3 formal
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Data collection and labeling are critical bottlenecks in the deployment of machine learning applications. With the increasing complexity and diversity of applications, the need for efficient and scalable data collection and labeling techniques has become paramount. This paper provides a review of the state-of-the-art methods in data collection, data labeling, and the improvement of existing data and models. By integrating perspectives from both the machine learning and data management communities, we aim to provide a holistic view of the current landscape and identify future research directions.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RawMal-TF: Raw Malware Dataset Labeled by Type and Family

    cs.CR 2025-06 conditional novelty 5.0 of 10

    RawMal-TF is a new public dataset of raw Windows malware binaries labeled by 14 behavioral types and 17 families, with EMBER static features and classification benchmarks.

  2. Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks

    cs.LG 2025-06 accept novelty 3.0 of 10

    A position paper synthesises 3GPP Release 18/19 AI/ML discussions and recommends hybrid, modular machine-learning directions for the 6G air interface.

  3. MedHallBench: A New Benchmark for Assessing Hallucination in Medical Large Language Models

    cs.CL 2024-12 reject novelty 2.0 of 10

    The paper proposes MedHallBench and ACHMI for medical hallucination measurement, but provides no dataset or code, and ACHMI is an uncredited replication of CHAIR.

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