Genetic relationships and population structure of 8 horse breeds in the Czech and Slovak Republics were investigated using classification methods for breed discrimination. To demonstrate genetic differences among these breeds, we used genetic information — genotype data of microsatellite markers and classification algorithms — to perform a probabilistic prediction of an individual’s breed. In total, 932 unrelated animals were genotyped for 17 microsatellite markers recommended by the ISAG for parentage testing (AHT4, AHT5, ASB2, HMS3, HMS6, HMS7, HTG4, HTG10, VHL20, HTG6, HMS2, HTG7, ASB17, ASB23, CA425, HMS1, LEX3). Algorithms of classification methods — J48 (decision trees); Naive Bayes, Bayes Net (probability predictors); IB1, IB5 (instance-based machine learning methods); and JRip (decision rules) — were used for analysis of their classification performance and of results of classification on this genotype dataset. Selected classification methods (Naive Bayes, Bayes Net, IB1), based on machine learning and principles of artificial intelligence, appear usable for these tasks.

中文翻译：

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使用机器学习方法区分马品种。

使用分类方法研究了捷克和斯洛伐克共和国 8 个马品种的遗传关系和种群结构。为了证明这些品种之间的遗传差异，我们使用遗传信息——微卫星标记的基因型数据和分类算法——对个体品种进行概率预测。总共对 932 只无关动物进行了 ISAG 推荐用于亲子鉴定的 17 个微卫星标记的基因分型（AHT4、AHT5、ASB2、HMS3、HMS6、HMS7、HTG4、HTG10、VHL20、HTG6、HMS2、HTG7、ASB17、ASB23、CA HMS1，LEX3）。分类方法的算法——J48（决策树）；朴素贝叶斯，贝叶斯网络（概率预测器）；IB1、IB5（基于实例的机器学习方法）；和 JRip（决策规则）——用于分析它们的分类性能和对该基因型数据集的分类结果。基于机器学习和人工智能原理的选定分类方法（朴素贝叶斯、贝叶斯网络、IB1）似乎可用于这些任务。