[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118619-en":3,"doc-seo-118619-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118619,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Data Collection and Labeling Techniques for Machine Learning","Data collection and labeling form critical bottlenecks for deploying machine learning applications, especially as real-world tasks grow more complex and diverse. This review synthesizes state-of-the-art methods for data collection, data labeling, and for improving existing data and models, combining insights from machine learning and data management communities. The work discusses practical challenges in scale, cost, privacy, and bias, highlights workflow integration via cleaning, standardization, and versioning, and outlines future research directions such as semi-supervised and unsupervised learning and bias detection.","arXiv :2407 . 12793v1 [ cs .DB] 19 Jun 2024  \nData Collection and Labeling Techniques for Machine Learning  \nQIANYU HUANG, National Taiwan University of Science and Technology, Taiwan TONGFANG ZHAO, National Taiwan University of Science and Technology, Taiwan  \nData 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.  \nACM Reference Format:  \nQianyu Huang and Tongfang Zhao. 2024. Data Collection and Labeling Techniques for Machine Learning. In Proceedings of Make sure to enter the correct conference title from your rights confirmation emai (Conference acronym ’XX). ACM, New York, NY, USA, 15 pages. [https://doi.org/XXXXXXX.XXXXXXX](https://doi.org/XXXXXXX.XXXXXXX)  \n1 Introduction  \nThe realm of machine learning (ML) has undergone a period of unprecedented growth in recent years. This remarkable advancement can be attributed to two key factors: the exponential rise in computational power and the ever-increasing availability of vast datasets [1–3] . However, the very foundation upon which this progress rests – data collection and labeling – presents significant challenges that can hinder theefficacyand ethical implementation ofML models[4–8]. This review paper delves into the intricate world of data collection and labeling for machine learning, drawing upon insights from both the data management and machine learning communities.  \nThe transformative potential of machine learning is evident across a multitude of domains. From revolutionizing healthcare with disease diagnosis and personalized medicine [9] to powering selfdriving cars [10] and optimizing logistics in supply chains [11], ML algorithms are rapidly reshaping our world. At the heart of these advancements lies the ability of ML models to learn from data, identify patterns, and make predictions based on the information they have been exposed to. The quality and quantity of data used to train these models are paramount to their success. High-quality, diverse, and well-labeled data are essential for building robust and generalizable ML models that can perform effectively in real-world scenarios [12, 13] .  \nHowever, the process of collecting and labeling data for machine learning is far from straightforward. The sheer volume of data required to train complex models can be daunting, and the task of meticulously labeling each data point can be incredibly time-consuming and expensive. Furthermore, ethical considerations regarding data privacy and potential biases within datasets pose  \nAuthors’ Contact Information: Qianyu Huang, [qianyuhuang19@gmail.com](qianyuhuang19@gmail.com), National Taiwan University of Science and Technology, Taipei, Taiwan; Tongfang Zhao, National Taiwan University of Science and Technology, Taipei, Taiwan.  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifi[c permission and/or a fee. Request permissions from permissions@acm.org](c permission and/or a fee. Request permissions from permissions@acm.org).  \nConference acronym ’XX, June 03–05, 2024, Woodstock, NY  \n© 2024 C","cbCaimLNC52SFixn","https://ap.wps.com/l/cbCaimLNC52SFixn","pdf",227355,1,17,"English","en",105,"# Introduction\n## Motivation and significance of data collection and labeling\n## Challenges: scale, cost, privacy, and bias\n# Review scope and objectives\n## Techniques for collecting data across different types\n## Approaches for labeling: manual, automated, crowdsourcing\n## Data management integration: cleaning, standardization, versioning\n## Future directions: semi-supervised/unsupervised learning and bias mitigation","[{\"question\":\"Why are data collection and labeling considered bottlenecks in machine learning deployment?\",\"answer\":\"They directly determine data quality and availability, which are prerequisites for robust and generalizable models. As applications diversify and grow in complexity, efficient and scalable collection and labeling become harder and more critical.\"},{\"question\":\"What labeling strategies are covered in the review?\",\"answer\":\"The review analyzes manual labeling, automated labeling techniques, and crowdsourcing-based approaches. It also considers how improved data and models can result from combining these strategies.\"},{\"question\":\"How can data management practices improve data collection and labeling workflows?\",\"answer\":\"Practices such as data cleaning, standardization, and versioning can be integrated to enhance efficiency and ensure consistent data quality throughout the workflow.\"},{\"question\":\"What future research directions does the review highlight?\",\"answer\":\"It emphasizes reducing dependence on labeled data using semi-supervised and unsupervised learning techniques. It also focuses on developing robust methods for detecting and mitigating bias in datasets.\"}]","Data Collection and Labeling Techniques for Machine Learning | PDF",1785684554,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"data-collection-and-labeling-techniques-for-machine-learning","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/data-collection-and-labeling-techniques-for-machine-learning/118619/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Why are data collection and labeling considered bottlenecks in machine learning deployment?","Question",{"text":75,"@type":76},"They directly determine data quality and availability, which are prerequisites for robust and generalizable models. As applications diversify and grow in complexity, efficient and scalable collection and labeling become harder and more critical.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What labeling strategies are covered in the review?",{"text":80,"@type":76},"The review analyzes manual labeling, automated labeling techniques, and crowdsourcing-based approaches. It also considers how improved data and models can result from combining these strategies.",{"name":82,"@type":73,"acceptedAnswer":83},"How can data management practices improve data collection and labeling workflows?",{"text":84,"@type":76},"Practices such as data cleaning, standardization, and versioning can be integrated to enhance efficiency and ensure consistent data quality throughout the workflow.",{"name":86,"@type":73,"acceptedAnswer":87},"What future research directions does the review highlight?",{"text":88,"@type":76},"It emphasizes reducing dependence on labeled data using semi-supervised and unsupervised learning techniques. 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