On the stopping criteria for k-Nearest Neighbor in positive unlabeled time series classification problems

Positive unlabeled time series classification has become an important area during the last decade, as often vast amounts of unlabeled time series data are available but obtaining the corresponding labels is difficult. In this situation, positive unlabeled learning is a suitable option to mitigate th...

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Veröffentlicht in:	Information sciences 2016-01, Vol.328, p.42-59
Hauptverfasser:	González, Mabel, Bergmeir, Christoph, Triguero, Isaac, Rodríguez, Yanet, Benítez, José M
Format:	Artikel
Sprache:	eng
Schlagworte:	Boundaries Classification Criteria Estimates k-Nearest Neighbor Learning Plugs Positive unlabeled learning Proposals Self-training Time series Time series classification Transductive learning
Online-Zugang:	Volltext
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