Detecting outliers with one-class selective transfer machine

In this paper, we propose an outlier detection method from an unlabeled target dataset by exploiting an unlabeled source dataset. Detecting outliers has attracted attention of data miners for over two decades, since such outliers can be crucial in decision making, knowledge discovery, and fraud dete...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Knowledge and information systems 2020-05, Vol.62 (5), p.1781-1818
Hauptverfasser:	Fujita, Hirofumi, Matsukawa, Tetsu, Suzuki, Einoshin
Format:	Artikel
Sprache:	eng
Schlagworte:	Anomalies Computer Science Data analysis Data Mining and Knowledge Discovery Database Management Datasets Decision making Fraud Information Storage and Retrieval Information Systems and Communication Service Information Systems Applications (incl.Internet) IT in Business Learning Miners Outliers (statistics) Regular Paper Support vector machines Transfer machines
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!