Szczegóły publikacji
Opis bibliograficzny
Ensembles of heterogeneous concept drift detectors - experimental study / Michał Woźniak, Paweł Ksieniewicz, Bogusław CYGANEK, Krzysztof Walkowiak // W: Computer Information Systems and Industrial Management : 15th IFIP TC8 international conference, CISIM 2016 : Vilnius, Lithuania, September 14–16, 2016 : proceedings / eds. Khalid Saeed, Władysław Homenda. — Cham [etc.] : Springer ; IFIP International Federation for Information Processing, cop. 2016. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 9842). — ISBN: 978-3-319-45377-4; e-ISBN: 978-3-319-45378-1. — S. 538–549. — Bibliogr. s. 548–549, Abstr.
Autorzy (4)
- Woźniak Michał
- Ksieniewicz Paweł
- AGHCyganek Bogusław
- Walkowiak Krzysztof
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 101674 |
|---|---|
| Data dodania do BaDAP | 2016-11-18 |
| DOI | 10.1007/978-3-319-45378-1_48 |
| Rok publikacji | 2016 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | International Conference on Computer Information Systems and Industrial Management Applications 2016 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
For the contemporary enterprises, possibility of appropriate business decision making on the basis of the knowledge hidden in stored data is the critical success factor. Therefore, the decision support software should take into consideration that data usually comes continuously in the form of so-called data stream, but most of the traditional data analysis methods are not ready to efficiently analyze fast growing amount of the stored records. Additionally, one should also consider phenomenon appearing in data stream called concept drift, which means that the parameters of an using model are changing, what could dramatically decrease the analytical model quality. This work is focusing on the classification task, which is very popular in many practical cases as fraud detection, network security, or medical diagnosis. We propose how to detect the changes in the data stream using combined concept drift detection model. The experimental evaluations confirm its pretty good quality, what encourage us to use it in practical applications.
