SciELO - Scientific Electronic Library Online

vol.18 issue1Introducing Biases in Document ClusteringLearning with Online Drift Detection author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand




Related links

  • Have no similar articlesSimilars in SciELO


Computación y Sistemas

Print version ISSN 1405-5546


PASCUAL GONZALEZ, Damaris; VAZQUEZ MESA, Fernando Daniel  and  TORO POZO, Jorge Luis. Noise Detection and Learning Based on Current Information. Comp. y Sist. [online]. 2014, vol.18, n.1, pp.153-167. ISSN 1405-5546.

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.

        · abstract in Spanish     · text in Spanish     · Spanish ( pdf )


Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License