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Comparing machine learning classifiers and linear/logistic regression to explore the relationship between hand dimensions and demographic characteristics

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Comparing machine learning classifiers and linear/logistic regression to explore the relationship between hand dimensions and demographic characteristics. / Miguel-Hurtado, Oscar; Guest, Richard; Stevenage, Sarah V.; Neil, Greg J.; Black, Sue.

In: PLoS ONE, Vol. 11, No. 11, e0165521, 02.11.2016.

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Miguel-Hurtado, Oscar ; Guest, Richard ; Stevenage, Sarah V. ; Neil, Greg J. ; Black, Sue. / Comparing machine learning classifiers and linear/logistic regression to explore the relationship between hand dimensions and demographic characteristics. In: PLoS ONE. 2016 ; Vol. 11, No. 11.

Bibtex

@article{a5872141976e4844b5f5dbc1242067de,
title = "Comparing machine learning classifiers and linear/logistic regression to explore the relationship between hand dimensions and demographic characteristics",
abstract = "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.",
author = "Oscar Miguel-Hurtado and Richard Guest and Stevenage, {Sarah V.} and Neil, {Greg J.} and Sue Black",
year = "2016",
month = nov,
day = "2",
doi = "10.1371/journal.pone.0165521",
language = "English",
volume = "11",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "11",

}

RIS

TY - JOUR

T1 - Comparing machine learning classifiers and linear/logistic regression to explore the relationship between hand dimensions and demographic characteristics

AU - Miguel-Hurtado, Oscar

AU - Guest, Richard

AU - Stevenage, Sarah V.

AU - Neil, Greg J.

AU - Black, Sue

PY - 2016/11/2

Y1 - 2016/11/2

N2 - 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.

AB - 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.

U2 - 10.1371/journal.pone.0165521

DO - 10.1371/journal.pone.0165521

M3 - Journal article

VL - 11

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 11

M1 - e0165521

ER -