Szczegóły publikacji
Opis bibliograficzny
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification / Michał KOZIARSKI, Colin Bellinger, Michał Woźniak // W: DSAA'2021 [Dokument elektroniczny] : 8th international conference on Data Science and Advanced Analytics : 6–9 October 2021, Porto, Portugal : proceedings. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, cop. 2021. — e-ISBN: 978-1-6654-2099-0. — S. [1–2]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [2], Abstr.
Autorzy (3)
- AGHKoziarski Michał
- Bellinger Colin
- Woźniak Michał
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 140102 |
|---|---|
| Data dodania do BaDAP | 2022-05-10 |
| Tekst źródłowy | URL |
| DOI | 10.1109/DSAA53316.2021.9564135 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | IEEE International Conference on Data Science and Advanced Analytics 2021 |
Abstract
In this paper we propose Radial-Based Combined Cleaning and Resampling (RB-CCR) algorithm. RB-CCR utilizes the concept of class potential to refine the energy-based resampling approach of previously proposed CCR algorithm. In particular, RB-CCR exploits the class potential to accurately locate sub-regions of the data-space for synthetic oversampling. The category sub-region for oversampling can be specified as an input parameter to meet domain-specific needs or be automatically selected via cross-validation. The results of the conducted experimental study show that RB-CCR achieves a better precision-recall trade-off than CCR and generally outperforms the state-of-the-art resampling methods in terms of AUC and G-mean.
