OALDPC: oversampling approach based on local density peaks clustering for imbalanced classification

Abstract SMOTE has been favored by researchers in improving imbalanced classification. Nevertheless, imbalances within minority classes and noise generation are two main challenges in SMOTE. Recently, clustering-based oversampling methods are developed to improve SMOTE by eliminating imbalances with...
Ausführliche Beschreibung

Abstract SMOTE has been favored by researchers in improving imbalanced classification. Nevertheless, imbalances within minority classes and noise generation are two main challenges in SMOTE. Recently, clustering-based oversampling methods are developed to improve SMOTE by eliminating imbalances within minority classes and/or overcoming noise generation. Yet, they still suffer from the following challenges: a) some create more synthetic minority samples in large-size or high-density regions; b) most fail to remove noise from the training set; c) most heavily rely on more than one parameter; d) most can not handle non-spherical data; e) almost all adopted clustering methods are not very suitable for class-imbalanced data. To overcome the above issues of existing clustering-based oversampling methods, this paper proposes a novel oversampling approach based on local density peaks clustering (OALDPC). First, a novel local density peaks clustering (LDPC) is proposed to partition the class-imbalanced training set into separated sub-clusters with different sizes and densities. Second, a novel LDPC-based noise filter is proposed to identify and remove suspicious noise from the class-imbalanced training set. Third, a novel sampling weight is proposed and calculated by weighing the sample number and density of each minority class sub-cluster. Four, a novel interpolation method based on the sampling weight and LDPC is proposed to create more synthetic minority class samples in sparser minority class regions. Intensive experiments have proven that OALDPC outperforms 8 state-of-the-art oversampling techniques in improving F-measure and G-mean of Random Forest, Neural Network and XGBoost on synthetic data and extensive real benchmark data sets from industrial applications. Ausführliche Beschreibung

Autor*in:	Li, Junnan [verfasserIn] Zhu, Qingsheng

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Class-imbalanced learning Class-imbalanced classification Oversampling technique Local density peaks Natural neighbor

Anmerkung:

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Applied intelligence - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991, 53(2023), 24 vom: 30. Nov., Seite 30987-31017
Übergeordnetes Werk:	volume:53 ; year:2023 ; number:24 ; day:30 ; month:11 ; pages:30987-31017

Links:	Volltext

DOI / URN:	10.1007/s10489-023-05030-4

Katalog-ID:	SPR054192943