A fuzzy-genetic approach to breast cancer diagnosis

By Carlos Andrés Peña-Reyes, Moshe Sipper. 1999

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

What do we need to build explainable AI systems for the medical domain? (2017, background): “Very useful for building explainable systems in the medical domain is generally ge-netic programming (Koza, 1994; Pena-Reyes and Sipper, 1999; Tsakonas et al., 2004), and speciﬁcally evolutionary algorithms (Wang and Palade, 2011; Holzinger et al., 2014, 2016).”
A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis (2011, methodology): “In Pena-Reyes and Sipper (1999), fuzzy-GA method was employed and a classification accuracy of 97.36% was obtained.”
Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases (2002, result): “Other examples of RBs obtained by the GFRBCS and additional experimental results can be found in (Pena-Reyes and Sipper, 1999b).”
Support vector machines combined with feature selection for breast cancer diagnosis (2009, methodology): “In Pena-Reyes and Sipper (1999), the classification technique used fuzzy-GA method reaching a classification accuracy of 97.36%.”
Cartesian genetic programming encoded artificial neural networks: a comparison using three benchmarks (2013, unknown): “Fuzzy-GA [33] No 3.”
Breast cancer diagnosis using least square support vector machine (2007, methodology): “The accuracy obtained by Bennett and Blue who used support vector machine (SVM) (5 × CV) method was 97.2% [10] while by Nauck and Kruse was 95.06% with neuro-fuzzy techniques [11] and by Pena-Rayes and Sipper was 97.36% using fuzzy-GA method [12].”