[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117989-en":3,"doc-seo-117989-105":30,"detail-sidebar-cat-0-en-105":91},{"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},117989,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","How to Do Machine Learning with Small Data - A Review from an Industrial Perspective","Machine learning grows in relevance across science, industry, and everyday applications, yet tasks requiring complex inference are not always feasible when datasets are limited. This industrial-oriented review interprets “small data” by comparing it with big data and introducing a machine learning formalism. It summarizes key industrial uses, defines small-data characteristics, and presents five critical challenges: unlabeled data, imbalanced data, missing data, insufficient data, and rare events. The work further outlines considerations for domain representation and data acquisition, with a taxonomy of approaches for small-data settings.","How to Do Machine Learning with Small Data?  \n– A Review from an Industrial Perspective  \nIvan Kraljevski, Yong Chul Ju, Dmitrij Ivanov, Constanze Tschöpe, and Matthias Wolff  \narXiv :2311 .07126v1 [ cs .LG] 13 Nov 2023  \nAbstract—Artificial intelligence experienced a technological breakthrough in science, industry, and everyday life in the recent few decades. The advancements can be credited to the ever-increasing availability and miniaturization of computational resources that resulted in exponential data growth. However, because of the insufficient amount of data in some cases, employing machine learning in solving complex tasks is not straightforward or even possible. As a result, machine learning with small data experiences rising importance in data science and application in several fields. The authors focus on interpreting the general term of “small data” and their engineering and industrial application role. They give a brief overview of the most important industrial applications of machine learning and small data. Small data is defined in terms of various characteristics compared to big data, and a machine learning formalism was introduced. Five critical challenges of machine learning with small data in industrial applications are presented: unlabeled data, imbalanced data, missing data, insufficient data, and rare events. Based on those definitions, an overview of the considerations in domain representation and data acquisition is given along with a taxonomy of machine learning approaches in the context of small data.  \nIndex Terms—machine learning, small data, industrial applications, engineering applications  \nI. INTRODUCTION  \nMachine learning became the most popular buzzword nowadays in business meetings and is often propagated as the magic novel cure for all problems and the carrier of the digital revolution. However, the general term machine learning contains a variety of data-driven methods which are fundamental for“artificial intelligence”(AI) [1], coined by John McCarthy [2] and pushed by Alan Turing’s question “can machine think?”to the modern AI research known today [3], [4] . In different contexts, machine learning (ML) is also known as data mining, or predictive analysis [5] .  \nT. M. Mitchel [6] defines machine learning as: “A computer program is said to learn from experience E concerning some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E”. Here, classes of tasks T encompass different types of prediction and inference, the source of experience E is the available “a priori” knowledge about the domain, including  \nThis study was supported by the Ministry of Science, Research and Culture of Brandenburg through the Project Kognitive Materialdiagnostik under Grant 22-F241-03-FhG/007/001 . {Corresponding author: Ivan Kraljevski.}  \nIvan Kraljevski, Yong Chul Ju and Constanze Tschöpe are with Fraunhofer Institute for Ceramic Technologies and Systems, Fraunhofer IKTS, Dresden, Germany (e-mail: [ivan.kraljevski@ikts.fraunhofer.de](ivan.kraljevski@ikts.fraunhofer.de)).  \nDmitrij Ivanov, Matthias Wolff are with Brandenburg University of Technology Cottbus–Senftenberg, Cottbus, Germany (e-mail: matthias.wolff@b[tu.de](tu.de)) .  \ndomain-specific data samples, and the P is a measure of success achieved in a particular task T. A less formal definition is given in [7], machine learning (ML) “is a sub-field of computer science, but is often also referred to as predictive analytics, or predictive modeling”. Its goal and usage is to build new and/or leverage existing algorithms to learn from data, in order to build generalizable models that give accurate predictions, or to find patterns, particularly with new and unseen similar data”.  \nIn the last two decades, considerable progress was made in ML-science leveraging the growing computational power [4], [8], [9] and the vast amounts of data increasingly being available (big data), see [10] . Cons","cbCairFtLg6NKPya","https://ap.wps.com/l/cbCairFtLg6NKPya","pdf",818759,1,22,"English","en",105,"# Introduction\n## Definitions and context of machine learning\n## Meaning and interpretations of small data\n## Why small data matters in industrial tasks","[{\"question\":\"What does the review mean by “small data” in contrast to “big data”?\",\"answer\":\"“Small data” is treated as limited or constrained data where big-data-driven ML methods are often not feasible due to insufficient volume, high dimensionality, complexity, or cost, and it is discussed with multiple interpretations.\"},{\"question\":\"Why is machine learning difficult when data are scarce?\",\"answer\":\"With too little data, models may lack enough examples to achieve desired performance, and practical constraints such as missing or unlabeled information can prevent straightforward learning and validation.\"},{\"question\":\"Which five challenges of machine learning with small data are highlighted for industrial applications?\",\"answer\":\"The review presents unlabeled data, imbalanced data, missing data, insufficient data, and rare events as the five critical challenges.\"}]","How to Do Machine Learning with Small Data - A Review from an Industrial Perspective | PDF",1785680653,55,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"how-to-do-machine-learning-with-small-data-a-review-from-an-industrial-perspective","",{"@graph":36,"@context":85},[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/how-to-do-machine-learning-with-small-data-a-review-from-an-industrial-perspective/117989/",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],{"name":72,"@type":73,"acceptedAnswer":74},"What does the review mean by “small data” in contrast to “big data”?","Question",{"text":75,"@type":76},"“Small data” is treated as limited or constrained data where big-data-driven ML methods are often not feasible due to insufficient volume, high dimensionality, complexity, or cost, and it is discussed with multiple interpretations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is machine learning difficult when data are scarce?",{"text":80,"@type":76},"With too little data, models may lack enough examples to achieve desired performance, and practical constraints such as missing or unlabeled information can prevent straightforward learning and validation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which five challenges of machine learning with small data are highlighted for industrial applications?",{"text":84,"@type":76},"The review presents unlabeled data, imbalanced data, missing data, insufficient data, and rare events as the five critical challenges.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]