The fundamental role of density functions in the binary classification problem

In biomedicine, binary classification problems are involved in diagnostic but also, for instance, in personalized medicine. The objective is to use information for correctly allocating subjects in groups. Frequently, this information implies high-dimensional data. An adequate classification rule is...

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Vydané v:Journal of statistical computation and simulation Ročník 92; číslo 13; s. 2846 - 2861
Hlavný autor: Martínez-Camblor, Pablo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Abingdon Taylor & Francis 02.09.2022
Taylor & Francis Ltd
Predmet:
Asymptotic properties
Binary classification problem
Classification
Density
Distribution functions
kernel density estimator
machine learning
Probability distribution functions
robust estimator
Support vector machines
ISSN:0094-9655, 1563-5163
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Abstract In biomedicine, binary classification problems are involved in diagnostic but also, for instance, in personalized medicine. The objective is to use information for correctly allocating subjects in groups. Frequently, this information implies high-dimensional data. An adequate classification rule is a trade-off between the sensitivity and the specificity. The ROC curve helps to understand, evaluate and compare the accuracy of classification processes. We propose a procedure for estimating the optimal classification rules based on a penalized estimator of the underlying probability distribution functions. We study its asymptotic properties. Through Monte Carlo simulations, we compare our proposal with a support vector machine-based ROC curve. We illustrate its practical use in a real-world problem. Results suggest that, despite some techniques promise to improve the results provided by traditional methods, in the binary classification problem, the limit is the actual relationship among the density functions.
AbstractList In biomedicine, binary classification problems are involved in diagnostic but also, for instance, in personalized medicine. The objective is to use information for correctly allocating subjects in groups. Frequently, this information implies high-dimensional data. An adequate classification rule is a trade-off between the sensitivity and the specificity. The ROC curve helps to understand, evaluate and compare the accuracy of classification processes. We propose a procedure for estimating the optimal classification rules based on a penalized estimator of the underlying probability distribution functions. We study its asymptotic properties. Through Monte Carlo simulations, we compare our proposal with a support vector machine-based ROC curve. We illustrate its practical use in a real-world problem. Results suggest that, despite some techniques promise to improve the results provided by traditional methods, in the binary classification problem, the limit is the actual relationship among the density functions.
Author Martínez-Camblor, Pablo
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  organization: Geisel School of Medicine at Dartmouth
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SubjectTerms Asymptotic properties
Binary classification problem
Classification
Density
Distribution functions
kernel density estimator
machine learning
Probability distribution functions
robust estimator
Support vector machines
Title The fundamental role of density functions in the binary classification problem
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