دورية أكاديمية
Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics.
العنوان: | Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics. |
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المؤلفون: | Miguel-Hurtado O; School of Engineering and Digital Arts, University of Kent, Canterbury, United Kingdom., Guest R; School of Engineering and Digital Arts, University of Kent, Canterbury, United Kingdom., Stevenage SV; Department of Psychology, University of Southampton, Southampton, United Kingdom., Neil GJ; Department of Psychology, University of Southampton, Southampton, United Kingdom., Black S; Centre for Anatomy and Human Identification, University of Dundee, Dundee, United Kingdom. |
المصدر: | PloS one [PLoS One] 2016 Nov 02; Vol. 11 (11), pp. e0165521. Date of Electronic Publication: 2016 Nov 02 (Print Publication: 2016). |
نوع المنشور: | Comparative Study; Journal Article |
اللغة: | English |
بيانات الدورية: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
أسماء مطبوعة: | Original Publication: San Francisco, CA : Public Library of Science |
مواضيع طبية MeSH: | Demography/*methods , Hand/*anatomy & histology, Algorithms ; Female ; Humans ; Linear Models ; Logistic Models ; Machine Learning ; Male |
مستخلص: | Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications. Competing Interests: The authors have declared that no competing interests exist. |
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تواريخ الأحداث: | Date Created: 20161103 Date Completed: 20170626 Latest Revision: 20181113 |
رمز التحديث: | 20231215 |
مُعرف محوري في PubMed: | PMC5091918 |
DOI: | 10.1371/journal.pone.0165521 |
PMID: | 27806075 |
قاعدة البيانات: | MEDLINE |
تدمد: | 1932-6203 |
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DOI: | 10.1371/journal.pone.0165521 |