Species distribution modeling and prediction: A class imbalance problem

Reid A. Johnson, N. Chawla, J. Hellmann
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引用次数: 36

Abstract

Predicting the distributions of species is central to a variety of applications in ecology and conservation biology. With increasing interest in using electronic occurrence records, many modeling techniques have been developed to utilize this data and compute the potential distribution of species as a proxy for actual observations. As the actual observations are typically overwhelmed by non-occurrences, we approach the modeling of species' distributions with a focus on the problem of class imbalance. Our analysis includes the evaluation of several machine learning methods that have been shown to address the problems of class imbalance, but which have rarely or never been applied to the domain of species distribution modeling. Evaluation of these methods includes the use of the area under the precision-recall curve (AUPR), which can supplement other metrics to provide a more informative assessment of model utility under conditions of class imbalance. Our analysis concludes that emphasizing techniques that specifically address the problem of class imbalance can provide AUROC and AUPR results competitive with traditional species distribution models.
物种分布建模与预测:一类不平衡问题
预测物种的分布对生态学和保护生物学的各种应用至关重要。随着人们对使用电子发生记录的兴趣的增加,许多建模技术已经开发出来,利用这些数据和计算物种的潜在分布作为实际观测的代理。由于实际观察结果通常被不发生的情况所淹没,我们将重点放在物种不平衡问题上,来建立物种分布的模型。我们的分析包括对几种机器学习方法的评估,这些方法已被证明可以解决类不平衡问题,但很少或从未应用于物种分布建模领域。对这些方法的评估包括使用精确召回率曲线下的面积(AUPR),它可以补充其他指标,以提供在类别不平衡条件下更有信息的模型效用评估。我们的分析得出结论,强调专门解决类失衡问题的技术可以提供AUROC和AUPR结果,与传统的物种分布模型相比具有竞争力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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