TY - GEN
T1 - Unsupervised Fuzzy eIX
T2 - 12th IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2020
AU - Aguiar, Charles
AU - Leite, Daniel
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - 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.
AB - 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.
KW - Evolving Fuzzy System
KW - Granular Computing
KW - Online Data Stream
KW - Unsupervised Learning
UR - http://www.scopus.com/inward/record.url?scp=85088097891&partnerID=8YFLogxK
U2 - 10.1109/EAIS48028.2020.9122774
DO - 10.1109/EAIS48028.2020.9122774
M3 - Conference contribution
AN - SCOPUS:85088097891
T3 - IEEE Conference on Evolving and Adaptive Intelligent Systems
BT - 2020 IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2020 - Proceedings
A2 - Castellano, Giovanna
A2 - Castiello, Ciro
A2 - Mencar, Corrado
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 May 2020 through 29 May 2020
ER -