Stream mining: a novel architecture for ensemble-based classification. (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• Stream mining: a novel architecture for ensemble-based classification. (Articolo in rivista) (literal)

Anno

• 2012-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1007/s10115-011-0378-4 (literal)

Alternative label

• Grossi V., Turini F. (2012)
  Stream mining: a novel architecture for ensemble-based classification.
  in Knowledge and Information Systems; Springer, London (Regno Unito)
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Grossi V., Turini F. (literal)

Pagina inizio

• 247 (literal)

Pagina fine

• 281 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

• http://link.springer.com/article/10.1007%2Fs10115-011-0378-4 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 30 (literal)

Rivista

• Knowledge and Information Systems (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

• 2 (literal)

Note

• ISI Web of Science (WOS) (literal)
• Scopu (literal)
• PuMa (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• Computer Science Department, University of Pisa, Italy; Computer Science Department, University of Pisa, Italy; (literal)

Titolo

• Stream mining: a novel architecture for ensemble-based classification. (literal)

Abstract

• Mining data streams has become an important and challenging task for a wide range of applications. In these scenarios, data tend to arrive in multiple, rapid and time-varying streams, thus constraining data mining algorithms to look at data only once. Maintaining an accurate model, e.g. a classifier, while the stream goes by requires a smart way of keeping track of the data already passed away. Such a synthetic structure has to serve two purposes: distilling the most of information out of past data and allowing a fast reaction to concept drifting, i.e. to the change of the data trend that necessarily affects the model. The paper outlines novel data structures and algorithms to tackle the above problem, when the model mined out of the data is a classifier. The introduced model and the overall ensemble architecture are presented in details, even considering how the approach can be extended for treating numerical attributes. A large part of the paper discusses the experiments and the comparisons with several existing systems. The comparisons show that the performance of our system in general, and in particular with respect to the reaction to concept drifting, is at the top level (literal)

Editore

• Springer (Editore)

Prodotto di

• Knowledge Discovery and Data Mining (ICT.P08.009.003) (Modulo)
• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)

Autore CNR

• FRANCO TURINI (Persona)

Insieme di parole chiave

• Keywords of "Stream mining: a novel architecture for ensemble-based classification." (Insieme di parole chiave)

Incoming links:

Prodotto

• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)
• Knowledge Discovery and Data Mining (ICT.P08.009.003) (Modulo)

Autore CNR di

• FRANCO TURINI (Persona)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Knowledge and Information Systems (Rivista)

Editore di

• Springer (Editore)

Insieme di parole chiave di

• Keywords of "Stream mining: a novel architecture for ensemble-based classification." (Insieme di parole chiave)