A Vector Representation of Lactation Curves for Dairy Cows

Machine learning techniques provide efficient data analysis tools without mathematical derivations. Data-centric LC representations are highly demanded to use these tools for LC-related research. A novel data-oriented LC representation model using piecewise linear regression (PWLR) is presented. Thi...

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Main Authors: Seonghun Lee, Jaehwa Park
Format: Article
Language:English
Published: MDPI AG 2022-03-01
Series:Agriculture
Subjects:
Online Access:https://www.mdpi.com/2077-0472/12/3/395
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author Seonghun Lee
Jaehwa Park
author_facet Seonghun Lee
Jaehwa Park
author_sort Seonghun Lee
collection DOAJ
description Machine learning techniques provide efficient data analysis tools without mathematical derivations. Data-centric LC representations are highly demanded to use these tools for LC-related research. A novel data-oriented LC representation model using piecewise linear regression (PWLR) is presented. This representation is intended to be used directly as data for machine learning along with other associated data at an individual base. An LC is represented in vector form as a series of connected line segments and the location and number of segments are determined by the maximum residual. The critical points are determined at the rapid transit point in the LC. The Bayesian information criterion was used to choose the proper number of line segments to avoid the overfitting problem. To demonstrate the validity of the PWLR model as an LC descriptor, its approximation accuracy and representation generality were tested experimentally. The results revealed that the PWLR model is advantageous for representing the LCs of an individual or a large herd that are directly applicable to data-driven approaches.
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spelling doaj.art-5ed6b3f2d7424654af1026ea9fa0fffb2023-11-24T00:05:42ZengMDPI AGAgriculture2077-04722022-03-0112339510.3390/agriculture12030395A Vector Representation of Lactation Curves for Dairy CowsSeonghun Lee0Jaehwa Park1Department of Software, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, KoreaDepartment of Software, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, KoreaMachine learning techniques provide efficient data analysis tools without mathematical derivations. Data-centric LC representations are highly demanded to use these tools for LC-related research. A novel data-oriented LC representation model using piecewise linear regression (PWLR) is presented. This representation is intended to be used directly as data for machine learning along with other associated data at an individual base. An LC is represented in vector form as a series of connected line segments and the location and number of segments are determined by the maximum residual. The critical points are determined at the rapid transit point in the LC. The Bayesian information criterion was used to choose the proper number of line segments to avoid the overfitting problem. To demonstrate the validity of the PWLR model as an LC descriptor, its approximation accuracy and representation generality were tested experimentally. The results revealed that the PWLR model is advantageous for representing the LCs of an individual or a large herd that are directly applicable to data-driven approaches.https://www.mdpi.com/2077-0472/12/3/395lactation curvepiecewise linear regressionvector representation
spellingShingle Seonghun Lee
Jaehwa Park
A Vector Representation of Lactation Curves for Dairy Cows
Agriculture
lactation curve
piecewise linear regression
vector representation
title A Vector Representation of Lactation Curves for Dairy Cows
title_full A Vector Representation of Lactation Curves for Dairy Cows
title_fullStr A Vector Representation of Lactation Curves for Dairy Cows
title_full_unstemmed A Vector Representation of Lactation Curves for Dairy Cows
title_short A Vector Representation of Lactation Curves for Dairy Cows
title_sort vector representation of lactation curves for dairy cows
topic lactation curve
piecewise linear regression
vector representation
url https://www.mdpi.com/2077-0472/12/3/395
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