Evolving Fuzzy-Rule-Based Classifiers From Data Streams

By Plamen Parvanov Angelov, Xiaowei Zhou. IEEE transactions on fuzzy systems, 2008

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Deep rule-based classifier with human-level performance and characteristics (2018, methodology): “More recently, very efficient data-driven FRB classifiers were proposed which can learn autonomously from the data (streams) [2], [8], and self-evolve, however even they could not reach the levels of performance achieved by deep learning classifiers mainly because of their quite simple and small int”
Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios (2019, methodology): “The following principle is used in order to check whether I k is a new prototype [25], [26].”
Distinguishing cell types or populations based on the computational analysis of their infrared spectra (2010, methodology): “In diagnosis studies, pattern classification methods such as artificial neural networks, support vector machines and evolving fuzzy classifiers are applied to obtain sensitivity and specificity figure”