| 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  [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >] | |
| Pascual González, Damaris [Universidad de Ortiente > Faculty of Economics] | |
| Vázquez Mesa, Fernando [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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