Multisurface method of pattern separation for medical diagnosis applied to breast cytology.

By William H. Wolberg, Olvi L. Mangasarian. 1990

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Citations in the corpus

Interpretability via Model Extraction (2017, methodology): “Description of Problem Instance Absolute Relative Dataset Task Samples Features Model f T Tbase T Tbase breast cancer [31] classification 569 32 random forest 0.”
Kernel PCA for novelty detection (2007, methodology): “These data were collected by Dr. William H. Wolberg at the University of Wisconsin Hospitals in Madison [19].”
A novel kernel method for clustering (2005, background): “The Wisconsin’s breast cancer database, 3 proposed by Wolberg and Mangasarian [22] in 1990, collects 699 cases of diagnostic samples.”
An evolutionary artificial neural networks approach for breast cancer diagnosis (2002, methodology): “The original dataset was obtained by Wolberg and Mangasarian [33].”
Empirical evaluation of the improved Rprop learning algorithms (2003, background): “The cancer1 problem [27] is also a real-world classification task: Based on 9 inputs describing a tumor, the task is to classify it as either benign or malignant.”
Bayesian CART – Prior Speciﬁcation and Posterior Simulation – (2006, methodology): “The data were obtained from the University of California, Irvine repository of machine learning databases (ftp://ftp.ics.uci.edu/pub/machine-learning-databases) and originated in Wolberg and Mangasarian (1990).”
OPLS discriminant analysis: combining the strengths of PLS‐DA and SIMCA classification (2006, methodology): “The methodology is further illustrated by a public two-class data set investigating differences between benign and malignant forms of breast cancer (see, for instance Reference [26]).”
Ranking and combining multiple predictors without labeled data (2013, background): “418 (ISBN 0-309-02788-8)) [10]Margolin, A. A, et al. (2013) Systematic Analysis of Challenge-Driven Improvements in Molecular Prognostic Models for Breast Cancer Science Translational Medicine 5, 181 [11]Linstone, H & Turo, M. e. (1975) Delphi Method : Techniques and Applications. (Addison-Wesle”
Finding the Number of Clusters in a Dataset (2003, unknown): “The secondis the Wisconsin breast cancer dataset (Wolberg and Mangasarian 1990), which consists of measurementsof nine variablesfor each of 683 patients.”