Validation of a quantifier-based fuzzy classification system for breast cancer patients on external independent cohorts
Recent studies in breast cancer domains have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a variety of unsupervised learning techniques. Consensus among the clustering algorithms has been used to categorise patients into these specific groups, but oft...
Main Authors: | Soria, Daniele, Garibaldi, Jonathan M. |
---|---|
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2016
|
Online Access: | http://eprints.nottingham.ac.uk/40536/ http://eprints.nottingham.ac.uk/40536/ http://eprints.nottingham.ac.uk/40536/1/Soria_ICMLA2016_Camera_Ready.pdf |
Similar Items
-
A quantifier-based fuzzy classification system for breast cancer patients
by: Soria, Daniele, et al.
Published: (2013) -
Consensus clustering and fuzzy classification for breast cancer prognosis
by: Garibaldi, Jonathan M., et al.
Published: (2010) -
A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised fuzzy c-means
by: Lai, Daphne Teck Ching, et al.
Published: (2014) -
A comparison of three different methods for classification of breast cancer data
by: Soria, Daniele, et al.
Published: (2008) -
Interpretability indices for hierarchical fuzzy systems
by: Razak, T.R., et al.
Published: (2017)