- Tytuł:
- A novel drift detection algorithm based on features’ importance analysis in a data streams environment
- Autorzy:
-
Duda, Piotr
Przybyszewski, Krzysztof
Wang, Lipo - Powiązania:
- https://bibliotekanauki.pl/articles/1837417.pdf
- Data publikacji:
- 2020
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
data stream mining
random forest
features importance - Opis:
- The training set consists of many features that influence the classifier in different degrees. Choosing the most important features and rejecting those that do not carry relevant information is of great importance to the operating of the learned model. In the case of data streams, the importance of the features may additionally change over time. Such changes affect the performance of the classifier but can also be an important indicator of occurring concept-drift. In this work, we propose a new algorithm for data streams classification, called Random Forest with Features Importance (RFFI), which uses the measure of features importance as a drift detector. The RFFT algorithm implements solutions inspired by the Random Forest algorithm to the data stream scenarios. The proposed algorithm combines the ability of ensemble methods for handling slow changes in a data stream with a new method for detecting concept drift occurrence. The work contains an experimental analysis of the proposed algorithm, carried out on synthetic and real data.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 4; 287-298
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł