RT Conference Proceedings
T1 Ensemble method based on individual evolving classifiers
A1 Iglesias Martínez, José Antonio
A1 Ledezma Espino, Agapito Ismael
A1 Sanchis de Miguel, María Araceli
AB Abstract: Humans often seek a second or third opinion about an important matter. Then, a final decision is reached after weighing and combining these opinions. This idea is the base of the ensemble based systems. Ensembles of classifiers are well established as a method for obtaining highly accurate classifiers by combining less accurate ones. On the other hand, evolving classifiers are inspired by the idea of evolve their structure in order to adapt to the changes of the environment. In this paper, we present a proof-of-concept method for constructing an ensemble system based on Evolving Fuzzy Systems. The main contribution of this approach is that the base-classifiers are self-developing (evolving) Fuzzy-rule-based (FRB) classifiers. Thus, we present an ensemble system which is based on evolving classifiers and keeps the properties of the evolving approach classification of streaming data. It is important to clarify that the evolving classifiers are gradually developing but they are not genetic or evolutionary.
PB IEEE Computer Society
SN 978-1-4673-5855-2
YR 2013
FD 2013
LK https://hdl.handle.net/10016/23613
UL https://hdl.handle.net/10016/23613
LA eng
NO This work has been supported by the Spanish Governmentunder i-Support (Intelligent Agent Based Driver DecisionSupport) Project (TRA2011-29454-C03-03).
DS e-Archivo
RD 17 jul. 2024