Reference : Limpieza de ruido para clasificación basado en vecindad y cambios de concepto en el tiempo
Scientific journals : Article
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/28389
Limpieza de ruido para clasificación basado en vecindad y cambios de concepto en el tiempo
Spanish
[en] Noise cleaning for classification based on neighborhood and concept changes over time
Toro Pozo, Jorge Luis mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Pascual González, Damaris mailto [Universidad de Ortiente > Faculty of Economics]
Vázquez Mesa, Fernando mailto [Universidad de Oriente > Faculty of Economics]
Apr-2016
Revista Cubana de Ciencias Informaticas
10
2
1-13
Yes
International
1994-1536
2227-1899
[en] noise cleaning ; semi-supervised learning ; concept drift
[en] An important field within data mining and pattern recognition is
classification. Classification is necessary in a number nowadays-world
processes. Several works and methods have been proposed with the goal to
achieve classifiers to be more effective each time. However, most of them
consider the training sets to be perfectly clustered, without having into
account that incorrectly classified data might be in them. The process of
removing incorrectly classified objects is called noise cleaning. Obviously,
noise cleaning influences considerably in classification of new samples. In
this work, we present a neighborhood-based algorithm for noise cleaning on data
stream for classification. In addition, it considers the data distribution
changes that may occur on the time. It was measured, by several experiments,
the effect of the method on automatic building of training sets by using
databases from UCI repository and two synthetic ones. The obtained results show
prove the efficacy of the proposed noise cleaning strategy and its influence on
the right classification of new samples.
http://hdl.handle.net/10993/28389

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