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extinction’s prediction. This model allows us to
work on numerous factors simultaneously. Based on
this model we set up three experiences with different
species’ features on two datasets. Results confirmed
the impact of demographic and genetic factors on
prediction of species extinction and showed that
very good predictor can be built. We demonstrated
that a combination of these factors can improve the
prediction’s accuracy. Moreover, the accuracy of
validation set presented the general ability of
selected features in prediction of impendent
extinction of species.
In a next step, we want to focus on correlation
and dependency between features. For this purpose,
we have to work on the analysis of features’
interactions and on the extraction of biologically
significant rules. These rules will help to reveal the
priority and relation between features and provide
some insight about the biological mechanisms
involved in species’ extinction.
ACKNOWLEDGEMENTS
This work is supported by the NSERC grant
ORGPIN 341854, the CRC grant 950-2-3617 and
the CFI grant 203617 and is made possible by the
facilities of the Shared Hierarchical Academic
Research Computing Network (SHARCNET, www.
sharcnet.ca).
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WEKA, V3.6.4, http://www.cs.waikato.ac.nz/ml/weka/
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