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Optimizing Decision Tree Structures for Spectral Histopathology (SHP)


This paper reviews methods to arrive at optimum decision tree or label tree structures to analyze large SHP data sets. Supervised methods of analysis can utilize either sequential or (flat) multi-classifiers depending on the variance in the data, and on the number of spectral classes to be distinguished. For small number of spectral classes, multi-classifiers have been used in the past, but for analysis of datasets containing large numbers (20) of disease or tissue types, mixed decision tree structures were found to be advantageous. In these mixed structures, discrimination into classes and subclasses is achieved via a hierarchical decision/label tree structures.

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Publication details

The article was received on 15 Jul 2018, accepted on 12 Oct 2018 and first published on 12 Oct 2018

Article type: Paper
DOI: 10.1039/C8AN01303A
Citation: Analyst, 2018, Accepted Manuscript
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    Optimizing Decision Tree Structures for Spectral Histopathology (SHP)

    X. Mu, M. Kon, A. Ergin, S. Remiszewski and M. Diem, Analyst, 2018, Accepted Manuscript , DOI: 10.1039/C8AN01303A

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