[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123000-en":3,"doc-seo-123000-105":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},123000,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Do all roads lead to Rome? Studying distance measures in the context of machine learning - Overview","Many machine learning and data mining tasks depend on distance and similarity measures, yet existing literature covers this topic unevenly due to its broad scope. This paper reviews recent work and analyzes seven widely used measures, detailing their main properties and practical applications across common learning problems. Comparative experiments investigate relationships, performance differences, and the degradation of results under noise, while also measuring the execution time required by each measure.","Pattern Recognition 141 (2023) 109646  \nContents lists available at ScienceDirect  \nPattern Recognition  \njournal [homepage: www.elsevier.com/locate/patcog](homepage: www.elsevier.com/locate/patcog)  \n| Do all roads lead to Rome? Studying distance measures in the context of machine learning\u003Cbr>Eva Blanco-Malloa,∗, Laura Morán-Fernández a, Beatriz Remeseirob, 1, Verónica Bolón-Canedoa, 1\u003Cbr>a Department of Computer Science and Information Technologies, Universidade da Coruña, CITIC. Campus de Elviña s/n, 15071 A Coruña, Spain b Department of Computer Science, Universidad de Oviedo. Campus de Gijón s/n, 33203 Gijón, Spain |  |  |  |\n| --- | --- | --- | --- |\n| a r t i c l e i n f o |  | a b s t r a c t |  |\n| Article history:\u003Cbr>Received 14 March 2022\u003Cbr>Revised 2 March 2023\u003Cbr>Accepted 25 April 2023\u003Cbr>Available online 26 April 2023 |  | Many machine learning and data mining tasks are based on distance measures, so a large amount of literature addresses this aspect somehow. Due to the broad scope of the topic, this paper aims to provide an overview of the use of these measures in the most common machine learning problems, pointing out those aspects to consider to choose the most appropriate measure for a particular task. For this purpose, the most recent works addressing the subject were reviewed and seven of the most commonly used measures were analyzed, investigating in detail their main properties and applications. Different experiments were carried out to study their relationships and compare their performance. The degradation of the results in the presence of noise was also considered, as well as the execution time required by each measure.\u003Cbr>© 2023 The Authors. Published by Elsevier Ltd.\u003Cbr>This is an open access article under the CC BY-NC-ND license\u003Cbr>([http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)) |  |\n| Keywords:\u003Cbr>Distance measures\u003Cbr>Similarity measures Classification\u003Cbr>Clustering\u003Cbr>Machine learning |  |  |  |\n\n1. Introduction  \nDistance and similarity measures describe how far or close two objects are. Since many real-world problems are based on finding similarities between groups of objects or populations, the list of knowledge areas that make use of them is very extensive. Some examples are biology, physics, chemistry, geography, ecology, social sciences, anthropology, algebra, statistical mathematics, engineering, and computer science [1]. In particular, within artificial intelligence, and more specifically in machine learning (ML) and data mining, many techniques rely on the use of distance and similarity measures. In fact, the knowledge areas mentioned above often make use of these techniques to tackle different issues.  \nSome examples of significant subareas of ML and their relationship to distance measures are as follows: (1) in some classification [2] or regression [3] problems, it is necessary to calculate the distance between new examples and those available in the train-  \n∗ Corresponding author.:  \nE-mail addresses: [eva.blanco@udc.es](eva.blanco@udc.es) (E. Blanco-Mallo), [laura.moranf@udc.es](laura.moranf@udc.es) (L.  \nMorán-Fernández), bremeseiro@uniovi.es (B. Remeseiro), [vbolon@udc.es](vbolon@udc.es) (V. BolónCanedo) .  \n1 These authors jointly supervised this work.  \ning set, as well as the error in the predictions; (2) clustering algorithms require a similarity measure to group objects according to their attributes [4,5]; (3) in feature selection, the decision criterion is based on the distance between sets [6,7]; (4) in quantification problems, similarity measures are used to compare probability distributions and estimate the distribution of each class [8]; (5) in anomaly detection, the distance between the examples is measured according to a reference distribution to detect outliers [9,10];  \n(6) in information retrieval, objects similar to a reference object are searched [11,12]; (7) in active learning, distance measurements are used to ide","cbCainRoxoHvfZx4","https://ap.wps.com/l/cbCainRoxoHvfZx4","pdf",3226809,1,15,"English","en",105,"# Introduction\n## Distance and similarity measures in machine learning\n## Common machine learning problems using distance measures","[{\"question\":\"What is the main goal of the paper on distance measures in machine learning?\",\"answer\":\"To provide an overview of how distance measures are used in common machine learning problems, and to highlight aspects needed to choose an appropriate measure for a given task.\"},{\"question\":\"Which types of machine learning tasks rely on distance or similarity measures?\",\"answer\":\"The paper discusses applications including classification/regression, clustering, feature selection, quantification, anomaly detection, information retrieval, active learning, and transfer learning.\"},{\"question\":\"How does the paper evaluate the measures beyond describing properties?\",\"answer\":\"It conducts experiments to study relationships and compare performance, examines result degradation in the presence of noise, and considers execution time required by each measure.\"}]","Do all roads lead to Rome? 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