- Tytuł:
- Self-configuring hybrid evolutionary algorithm for fuzzy imbalanced classification with adaptive instance selection
- Autorzy:
-
Stanovov, V.
Semenkin, E.
Semenkina, O. - Powiązania:
- https://bibliotekanauki.pl/articles/91578.pdf
- Data publikacji:
- 2016
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
fuzzy classification
instance selection
genetic fuzzy system
self-configuration - Opis:
- A novel approach for instance selection in classification problems is presented. This adaptive instance selection is designed to simultaneously decrease the amount of computation resources required and increase the classification quality achieved. The approach generates new training samples during the evolutionary process and changes the training set for the algorithm. The instance selection is guided by means of changing probabilities, so that the algorithm concentrates on problematic examples which are difficult to classify. The hybrid fuzzy classification algorithm with a self-configuration procedure is used as a problem solver. The classification quality is tested upon 9 problem data sets from the KEEL repository. A special balancing strategy is used in the instance selection approach to improve the classification quality on imbalanced datasets. The results prove the usefulness of the proposed approach as compared with other classification methods.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 3; 173-188
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł