Artículos

 

Detección de ruido y aprendizaje basado en información actual

 

Noise Detection and Learning Based on Current Information

 

Damaris Pascual González¹, Fernando Daniel Vázquez Mesa¹ y Jorge Luis Toro Pozo²

 

¹ Facultad de Ciencias Económicas y Empresariales, Universidad de Oriente, Santiago de Cuba, Cuba. dpascual@eco.uo.edu.cu, fvazquez@eco.uo.edu.cu

² Facultad de Matemática y Computación, Universidad de Oriente, Santiago de Cuba, Cuba. jorgetp@ult.edu.cu

 

Resumen

Los métodos de limpieza de ruido tienen una gran significación en tareas de clasificación y en situaciones en las que es necesario realizar un aprendizaje semi-supervisado, debido a la importancia que tiene contar con muestras bien etiquetadas (prototipos) para clasificar nuevos patrones. En este trabajo, presentamos un nuevo algoritmo de detección de ruido en flujos de datos, que tiene en cuenta los cambios de los conceptos en el tiempo (concept drift), el cual está basado en criterios de vecindad, y su aplicación en la construcción automática de conjuntos de entrenamiento. En los experimentos realizados se utilizaron bases de datos sintéticas y reales, las últimas fueron tomadas del repositorio UCI, los resultados obtenidos avalan nuestra estrategia de detección de ruido en flujos de datos y en procesos de clasificación.

Palabras clave: Limpieza de ruido, flujo de datos, aprendizaje semisupervisado; concept drift.

 

Abstract

Methods for noise cleaning have great significance in classification tasks and in situations when it is necessary to carry out a semi-supervised learning due to importance of having well-labeled samples (prototypes) for classification of the new patterns. In this work, we present a new algorithm for detecting noise in data streams that takes into account changes in concepts over time (concept drift). The algorithm is based on the neighborhood criteria and its application uses the construction of a training set. In our experiments we used both synthetic and real databases, the latter were taken from UCI repository. The results support our proposal of noise detection in data streams and classification processes.

Keywords: Cleansing noise, data streams, semi-supervised learning, concept drift.

 

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