Gast ::
| Hauptseite > Publikationsdatenbank > A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data > print |
| Hauptseite > Publikationsdatenbank > A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data > print |
| 001 | 845280 | ||
| 005 | 20210129233344.0 | ||
| 024 | 7 | _ | |a 10.1016/j.neucom.2017.08.035 |2 doi |
| 024 | 7 | _ | |a 0925-2312 |2 ISSN |
| 024 | 7 | _ | |a 1872-8286 |2 ISSN |
| 024 | 7 | _ | |a 2128/18233 |2 Handle |
| 024 | 7 | _ | |a pmid:29398782 |2 pmid |
| 024 | 7 | _ | |a WOS:000418370200032 |2 WOS |
| 037 | _ | _ | |a FZJ-2018-02561 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Collell, Guillem |0 0000-0003-1998-8621 |b 0 |
| 245 | _ | _ | |a A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data |
| 260 | _ | _ | |a Amsterdam |c 2018 |b Elsevier |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1524488430_26620 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Class imbalance presents a major hurdle in the application of classification methods. A commonly taken approach is to learn ensembles of classifiers using rebalanced data. Examples include bootstrap averaging (bagging) combined with either undersampling or oversampling of the minority class examples. However, rebalancing methods entail asymmetric changes to the examples of different classes, which in turn can introduce their own biases. Furthermore, these methods often require specifying the performance measure of interest a priori, i.e., before learning. An alternative is to employ the threshold moving technique, which applies a threshold to the continuous output of a model, offering the possibility to adapt to a performance measure a posteriori, i.e., a plug-in method. Surprisingly, little attention has been paid to this combination of a bagging ensemble and threshold-moving. In this paper, we study this combination and demonstrate its competitiveness. Contrary to the other resampling methods, we preserve the natural class distribution of the data resulting in well-calibrated posterior probabilities. Additionally, we extend the proposed method to handle multiclass data. We validated our method on binary and multiclass benchmark data sets by using both, decision trees and neural networks as base classifiers. We perform analyses that provide insights into the proposed method. |
| 536 | _ | _ | |a 899 - ohne Topic (POF3-899) |0 G:(DE-HGF)POF3-899 |c POF3-899 |f POF III |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Prelec, Drazen |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Patil, Kaustubh |0 P:(DE-Juel1)172843 |b 2 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.1016/j.neucom.2017.08.035 |g Vol. 275, p. 330 - 340 |0 PERI:(DE-600)1479006-3 |p 330 - 340 |t Neurocomputing |v 275 |y 2018 |x 0925-2312 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/845280/files/Guillem18.pdf |
| 856 | 4 | _ | |y OpenAccess |x icon |u https://juser.fz-juelich.de/record/845280/files/Guillem18.gif?subformat=icon |
| 856 | 4 | _ | |y OpenAccess |x icon-1440 |u https://juser.fz-juelich.de/record/845280/files/Guillem18.jpg?subformat=icon-1440 |
| 856 | 4 | _ | |y OpenAccess |x icon-180 |u https://juser.fz-juelich.de/record/845280/files/Guillem18.jpg?subformat=icon-180 |
| 856 | 4 | _ | |y OpenAccess |x icon-640 |u https://juser.fz-juelich.de/record/845280/files/Guillem18.jpg?subformat=icon-640 |
| 856 | 4 | _ | |y OpenAccess |x pdfa |u https://juser.fz-juelich.de/record/845280/files/Guillem18.pdf?subformat=pdfa |
| 909 | C | O | |o oai:juser.fz-juelich.de:845280 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)172843 |
| 913 | 1 | _ | |a DE-HGF |b Programmungebundene Forschung |l ohne Programm |1 G:(DE-HGF)POF3-890 |0 G:(DE-HGF)POF3-899 |2 G:(DE-HGF)POF3-800 |v ohne Topic |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |
| 914 | 1 | _ | |y 2018 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1160 |2 StatID |b Current Contents - Engineering, Computing and Technology |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b NEUROCOMPUTING : 2015 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0111 |2 StatID |b Science Citation Index Expanded |
| 915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Thomson Reuters Master Journal List |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-7-20090406 |k INM-7 |l Gehirn & Verhalten |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)INM-7-20090406 |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|