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Evaluation of six machine learning classification algorithms in pig breed identification using SNPs array data.

Ruiqi Liu¹, Zhiting Xu¹, Jinyan Teng¹

¹National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou, China.

Animal Genetics

|December 3, 2022

Summary

This summary is machine-generated.

Machine learning accurately identifies pig breeds using genomic data. Feature selection with eXtreme Gradient Boosting achieved over 95% accuracy, demonstrating its effectiveness in animal genetics.

Keywords:

anomaly detection breed identification feature selection machine learning stacking ensemble

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Area of Science:

Animal Genetics and Breeding
Bioinformatics
Machine Learning Applications

Background:

Breed identification is crucial in animal genetics and breeding.
High-throughput genomic data and machine learning are increasingly integrated for this purpose.

Purpose of the Study:

To evaluate machine learning and stacking ensemble learning classifiers for pig breed identification.
To assess the impact of feature selection and anomaly detection on identification accuracy.

Main Methods:

Utilized genomic data from 654 individuals across 15 pig breeds.
Applied eXtreme Gradient Boosting (XGBoost) for feature selection and classification.
Evaluated stacking ensemble learning and local outlier factor (LOF) for anomaly detection.

Main Results:

Feature selection using XGBoost achieved >95% accuracy for nine breeds with 32 SNPs and 16 individuals/breed.
Stacking ensemble learning improved accuracy by 9.24% over the best base learner but was comparable to XGBoost feature selection.
Anomaly detection with LOF reached 89.06% accuracy for 15 breeds using 512 features and 16 individuals/breed.

Conclusions:

Machine learning, particularly with feature selection, is an effective tool for pig breed identification.
This study provides valuable insights for applying machine learning in animal genetics and breeding.
Genomic data combined with advanced algorithms significantly enhances breed identification accuracy.