Comprehensive Decision Tree Models in Bioinformatics
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TLDR
By building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm.Abstract:
Purpose
Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible.read more
Citations
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Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond
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A review of machine learning methods to predict the solubility of overexpressed recombinant proteins in Escherichia coli.
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References
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