Evolving clustering, classification and regression with TEDA

Kangin, Dmitry and Angelov, Plamen Parvanov (2015) Evolving clustering, classification and regression with TEDA. In: Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-8.

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Abstract

In this article the novel clustering and regression methods TEDACluster and TEDAPredict methods are described additionally to recently proposed evolving classifier TEDAClass. The algorithms for classification, clustering and regression are based on the recently proposed AnYa type fuzzy rule based system. The novel methods use the recently proposed TEDA framework capable of recursive processing of large amounts of data. The framework is capable of computationally cheap exact update of data per sample, and can be used for training `from scratch'. All three algorithms are evolving that is they are capable of changing its own structure during the update stage, which allows to follow the changes within the model pattern.

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ID Code:
77920
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Deposited On:
26 Jan 2016 14:22
Refereed?:
Yes
Published?:
Published
Last Modified:
29 Mar 2020 00:21