Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/49682
Title: Unsupervised Fuzzy eIX: clusterização interna-externa fuzzy evolutiva de fluxos de dados não-estacionários
Other Titles: Unsupervised Fuzzy eIX: evolving internal-external fuzzy clustering for non-stationary online data streams
Authors: Leite, Daniel
Leite, Daniel Furtado
Cordovil Junior, Luiz Alberto Queiroz
Camargos Filho, Murilo Cesar Osorio
Keywords: Aprendizado não supervisionado
Sistema Fuzzy evolutivo
Computação granular
Fluxos de dados online
Unsupervised learning
Evolving Fuzzy system
Granular computing
Online data stream
Issue Date: 6-Apr-2022
Publisher: Universidade Federal de Lavras
Citation: AGUIAR, C. C. de. Unsupervised Fuzzy eIX: clusterização interna-externa fuzzy evolutiva de fluxos de dados não-estacionários. 2022. 62 p. Dissertação (Mestrado em Engenharia de Sistemas e Automação) – Universidade Federal de Lavras, Lavras, 2022.
Abstract: Classifiers with time-varying decision boundaries, namely, evolving classifiers, play an important role in a scenario in which information is available as an online data stream. This text presents a new unsupervised learning method for numerical data called evolving Internal-eXternal Fuzzy clustering method (Fuzzy eIX). The notion of double-boundary fuzzy granules and some of its implications are developed and explored. It will be shown how type 1 and type 2 fuzzy inference systems can be obtained from the projection of Fuzzy eIX granules on orthogonal axes corresponding to the dimensions of a problem. Fuzzy eIX learning algorithm performs Pedrycz Balanced Information Granularity principle within fuzzy eIX classifiers to achieve a higher level of model understandability in a given problem domain. 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 preliminary problem called Rotation of Twin Gaussians shows the behavior of the classifier for a nonstationary data stream input. Additionally, the performance of the Fuzzy eIX method will be compared to other two evolving methods already established in the literature when it comes to the classification of benchmark data sets usually employed in online machine learning models assessments. Comparisons will also be conducted in terms of partition quality through incremental cluster validation indexes, the accuracy and compactness of the resulting rules structure.
URI: http://repositorio.ufla.br/jspui/handle/1/49682
Appears in Collections:Engenharia de Sistemas e automação (Dissertações)



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