Abstract
Search of selection at molecular level is typically done with neutrality tests. However, non selective factors can cause similar effects on tests results. In order to make the influence of particular non selective forces weaker, a battery of such tests can be applied and the combination of results could be considered as inputs to a properly trained classifier. Success of such approach depends on two issues: appropriate expert knowledge to be used during training of the classifier and the choice of the most suitable machine learning method, which generalizes the acquired knowledge to unknown cases. Comparison of connectionist and rule-based rough set approaches in this role is the main goal of the paper.
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Cyran, K.A., Kimmel, M. (2014). Comparison of Connectionist and Rough Set Based Knowledge Discovery Methods in Search for Selection in Genes Implicated in Human Familial Cancer. In: Gruca, D., Czachórski, T., Kozielski, S. (eds) Man-Machine Interactions 3. Advances in Intelligent Systems and Computing, vol 242. Springer, Cham. https://doi.org/10.1007/978-3-319-02309-0_17
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DOI: https://doi.org/10.1007/978-3-319-02309-0_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02308-3
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