@inproceedings{8972937bbcf4442387ac56dedbe30311,
title = "Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering",
abstract = "Time-varying classifiers, namely, evolving classifiers, play an important role in a scenario in which information is available as a never-ending online data stream. We present a new unsupervised learning method for numerical data called evolving Internal-eXternal Fuzzy clustering method (Fuzzy eIX). We develop the notion of double-boundary fuzzy granules and elaborate on its implications. Type 1 and type 2 fuzzy inference systems can be obtained from the projection of Fuzzy eIX granules. We perform the principle of the balanced information granularity within Fuzzy eIX classifiers to achieve a higher level of model understandability. Internal and external granules are updated from a numerical data stream at the same time that the global granular structure of the classifier is autonomously evolved. A synthetic nonstationary problem called Rotation of Twin Gaussians shows the behavior of the classifier. The Fuzzy eIX classifier could keep up with its accuracy in a scenario in which offline-trained classifiers would clearly have their accuracy drastically dropped.",
keywords = "Evolving Fuzzy System, Granular Computing, Online Data Stream, Unsupervised Learning",
author = "Charles Aguiar and Daniel Leite",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; null ; Conference date: 27-05-2020 Through 29-05-2020",
year = "2020",
month = may,
doi = "10.1109/EAIS48028.2020.9122774",
language = "English",
series = "IEEE Conference on Evolving and Adaptive Intelligent Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Giovanna Castellano and Ciro Castiello and Corrado Mencar",
booktitle = "2020 IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2020 - Proceedings",
}