دورية أكاديمية
أعرض في EDS

Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics.

التفاصيل البيبلوغرافية
العنوان: Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics.
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.
References: Clin Anat. 2005 Nov;18(8):589-96. (PMID: 16187319)
J Forensic Leg Med. 2010 Apr;17(3):156-60. (PMID: 20211457)
J Forensic Sci. 2007 Mar;52(2):264-70. (PMID: 17316220)
J Forensic Leg Med. 2015 Oct;35:9-14. (PMID: 26344451)
Am J Phys Anthropol. 2005 Aug;127(4):406-17. (PMID: 15624209)
J Forensic Sci. 1995 Sep;40(5):774-6. (PMID: 7595320)
Forensic Sci Int. 2015 Dec;257:521.e1-10. (PMID: 26597170)
Psychol Res. 2015 Nov;79(6):989-99. (PMID: 25410711)
Am J Phys Anthropol. 1978 Jan;48(1):113-9. (PMID: 623227)
Arch Sex Behav. 2009 Apr;38(2):298-305. (PMID: 18340520)
J Forensic Sci. 2009 May;54(3):546-50. (PMID: 19302378)
Hum Biol. 1983 Dec;55(4):867-83. (PMID: 6674108)
Forensic Sci Int. 2012 Apr 10;217(1-3):229.e1-8. (PMID: 22100328)
Forensic Sci Int. 2010 Jun 15;199(1-3):112.e1-6. (PMID: 20382487)
J Forensic Leg Med. 2008 Nov;15(8):479-82. (PMID: 18926497)
Biol Psychol. 2006 Jan;71(1):116-21. (PMID: 16360883)
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
الوصف
تدمد:1932-6203
DOI:10.1371/journal.pone.0165521