RIONIDA: A Novel Algorithm for Imbalanced Data Combining Instance-Based Learning and Rule Induction

被引：0

作者：

Gora, Grzegorz ^{[1
]}

Skowron, Andrzej ^{[2
]}

机构：

[1] Univ Warsaw, Stefana Banacha 2, PL-02097 Warsaw, Poland

[2] PAS, Syst Res Inst, Newelska 6, PL-01447 Warsaw, Poland

来源：

ROUGH SETS, PT I, IJCRS 2024 | 2024年 / 14839卷

关键词：

Imbalanced Learning; Classification; Supervised Learning; Instance-based Learning; k Nearest Neighbours; Rule Induction;

D O I：

10.1007/978-3-031-65665-1_13

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The article presents the RIONIDA learning algorithm based on combination of two widely-used empirical approaches: rule induction and instance-based learning for imbalanced data classification. The algorithm is a substantial extension of the well-known RIONA algorithm developed for balanced data. RIONIDA is relatively fast and significantly outperforms the state-of-the-art algorithms analysed in the paper.

引用

页码：201 / 219

页数：19

共 37 条

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