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Background — Problem Setup

Forming knowledge of where species live is important, with applications in ecological conservation and sustainability. Modeling techniques that estimate the potential distribution of species are used as proxies for actual observations.

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Background — Problem Setup

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  1. Forming knowledge of where species live is important, with applications in ecological conservation and sustainability. • Modeling techniques that estimate the potential distribution of species are used as proxies for actual observations. • Species distribution modeling is the process of combining occurrence data with environmentalvariablesto create a model of a species’ niche requirements.

  2. Background — Problem Setup Notice the imbalance that exists between the number of occurrences (green) and the number of non-occurrences (non-green). We can term this as a “class imbalance”.

  3. Problem Attributes • What are the problem attributes? • Data characterized by two classes • Severe data imbalance • Many instances

  4. Hellinger Distance Decision Trees • HDDTs are a method of building decision trees using Hellinger distance as the splitting criterion: , , , are the classes of interest is the number of data partitions = number of samples in partition = number of samples of class , , respectively = number of samples of class , in partition Cieslak, David A., et al. "Hellinger distance decision trees are robust and skew-insensitive." Data Mining and Knowledge Discovery 24.1 (2012): 136-158.

  5. Environmental Features BIOCLIM http://www.worldclim.org/bioclim

  6. Model Evaluation • AUROC: • True positive rate: fraction of positive instances classified positive • False positive rate: fraction of negative instances classified negative • AUPR: • Precision:  fraction of retrieved instances that are relevant • Recall:  fraction of relevant instances that are retrieved • CORR: • Correlation between observation and prediction • Computed as a Pearson correlation coefficient

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