Class-Based Outlier Detection: Staying Zombies or Awaiting for Resurrection?

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2015

Autores

Leona Nezvalová (Autor) Outra; Lubos Popelínsky (Autor) Outra; Luis Torgo (Autor) FCUP; Karel Vaculík (Autor) Outra

Ata de Conferência Internacional

Título: Advances in Intelligent Data Analysis XIV

Páginas: 193-204

14th International Symposium on Intelligent Data Analysis (IDA)

Saint Etienne, FRANCE, OCT 22-24, 2015

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00G-SZE

DOI: 10.1007/978-3-319-24465-5_17

Abstract (EN): This paper addresses the task of finding outliers within each class in the context of supervised classification problems. Class-based outliers are cases that deviate too much with respect to the cases of the same class. We introduce a novel method for outlier detection in labelled data based on Random Forests and compare it with existing methods both on artificial and real-world data. We show that it is competitive with the existing methods and sometimes gives more intuitive results. We also provide an overview for outlier detection in labelled data. The main contribution are two methods for class-based outlier description and interpretation.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12