Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Kongresse
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Veranstaltungskalender Awards MyNewsletter Firmensuche
Kunden Login
Über Einzelzugang informieren
Über Unternehmenszugang informieren
Referenzkunden
Heavy-Duty Engines 2025 | 18. + 19. November 2025

Nutzfahrzeuge | Land- und Baumaschinen | Marine

Nehmen Sie als Referent an der 20. Internationalen MTZ-Fachtagung für Erfahrungsaustausch zu Industrie- und Nfz-Motoren teil. Reichen Sie online einen Themenvorschlag ein!
Erweiterte Suche
Anmelden
nach oben
Journal of Classification
Erschienen in:

23.02.2023

Finding the Proverbial Needle: Improving Minority Class Identification Under Extreme Class Imbalance

verfasst von: Trent Geisler, Herman Ray, Ying Xie

Erschienen in: Journal of Classification | Ausgabe 1/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Imbalanced learning problems typically consist of data with skewed class distributions, coupled with large misclassification costs for the rare events. For binary classification, logistic regression is a common supervised learning technique chosen to perform this task. Unfortunately, the model performs poorly on classification tasks when class distributions are highly imbalanced. To improve this generalization, we implement a novel instance-level weighting methodology for the minority class in the loss function. We build our method from a recently published, locally weighted log-likelihood objective function, where each of the minority class weights are learned from the data. We improve upon this previous approach by creating a convex and hyperparameter-free loss function that improves generalization performance for datasets exhibiting extreme class imbalance.
Vorheriger Artikel Classification Trees with Mismeasured Responses

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Anhänge
Nur mit Berechtigung zugänglich
Fußnoten
1
The model training process is synonymous with the term optimization process, where the parameter estimates are updated to find the minimum overall loss value. Since we have a convex loss function, we can use a wide range of optimization algorithms during the training process. In this particular instance, we use Python’s SciPy’s optimize module to minimize the loss function with the Newton-CG method (Virtanen et al., 2020).
 
2
The data that support the findings of this study are available at https://​github.​com/​x46182/​proverbial_​needle_​manuscript.
 
3
The threshold value where sensitivity is equal to specificity is also evaluated.
 
Literatur
Beyan, C., & Fisher, R. (2015). Classifying imbalanced data sets using similarity based hierarchical decomposition. Pattern Recognition, 48(5), 1653–1672.CrossRef
Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.CrossRefMATH
Branco, P., Torgo, L., & Ribeiro, R. P. (2016). A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR), 49(2), 1–50.CrossRef
Cortez, P., Cerdeira, A., Almeida, F., Matos, T, & Reis, J. (2009). Wine quality. UCI Machine Learning Repository.
Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on machine learning (pp. 233–240).
Denil, M., & Trappenberg, T. (2010). Overlap versus imbalance. Canadian conference on artificial intelligence (pp. 220–231).
Fernández, A., García, S., Galar, M., Prati, R. C., Krawczyk, B., & Herrera, F. (2018). Learning from imbalanced data sets Vol. 11. New York: Springer.CrossRef
Geisler, T., Priestley, J., Ray, H., & Xie, Y. (2022). Improving minority class detection in imbalanced learning applications with a logistic regression novel loss function. Manuscript Under Development.
Geisler, T., Ray, H, & Xie, Y. (2022). Novel neural network loss for extreme class imbalance. Manuscript Under Development.
Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., & Bing, G. (2017). Learning from class-imbalanced data: review of methods and applications. Expert Systems with Applications, 73, 220–239.CrossRef
He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284.CrossRef
Japkowicz, N., & Stephen, S. (2002). The class imbalance problem: a systematic study. Intelligent Data Analysis, 6(5), 429–449.CrossRefMATH
King, G., & Zeng, L. (2001). Logistic regression in rare events data. Political Analysis, 9(2), 137–163.CrossRef
Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied linear statistical models Vol. 5. Boston: McGraw-Hill Irwin.
Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. Proceedings of the IEEE international conference on computer vision (pp. 2980–2988).
Loyola-González, O., Martínez-Trinidad, J. F., Carrasco-Ochoa, J. A., & García-Borroto, M. (2016). Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases. Neurocomputing, 175, 935–947.CrossRef
López, V., Fernández, A., García, S., Palade, V., & Herrera, F (2013). An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Information Sciences, 250, 113–141.CrossRef
Maalouf, M., Homouz, D., & Trafalis, T. B. (2018). Logistic regression in large rare events and imbalanced data: a performance comparison of prior correction and weighting methods. Computational Intelligence, 34(1), 161–174.MathSciNetCrossRef
Maloof, M. A. (2003). Learning when data sets are imbalanced and when costs are unequal and unknown. ICML-2003 workshop on learning from imbalanced data sets II (Vol. 2 pp. 2–1).
Manski, C. F., & Lerman, S. R. (1977). The estimation of choice probabilities from choice based samples. Econometrica: Journal of the Econometric Society 1977–1988.
Roberts, A. W. (1993). Convex functions. Handbook of convex geometry, (pp. 1081–1104). Amsterdam: Elsevier.CrossRefMATH
Stefanowski, J. (2016). Dealing with data difficulty factors while learning from imbalanced data. Challenges in computational statistics and data mining (pp. 333–363). Springer.
Wallace, B. C., Small, K., Brodley, C. E., & Trikalinos, T. A. (2011). Class imbalance, redux. 2011 IEEE 11th international conference on data mining (pp. 754–763).
Yang, X., Song, Q., & Wang, Y. (2007). A weighted support vector machine for data classification. International Journal of Pattern Recognition and Artificial Intelligence, 21(05), 961–976.CrossRef
Zhang, L., Priestley, J., & Ni, X. (2018). Influence of the event rate on discrimination abilities of bankruptcy prediction models. arXiv:1803.​03756.
Metadaten
Titel
Finding the Proverbial Needle: Improving Minority Class Identification Under Extreme Class Imbalance
verfasst von
Trent Geisler
Herman Ray
Ying Xie
Publikationsdatum
23.02.2023
Verlag
Springer US
Erschienen in
Journal of Classification / Ausgabe 1/2023
Print ISSN: 0176-4268
Elektronische ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-023-09431-5

Weitere Artikel der Ausgabe 1/2023

Classification Trees with Mismeasured Responses

Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions

Uncertainty Diagnostics of Binomial Regression Trees for Ordered Rating Data

  • Open Access

DDCAL: Evenly Distributing Data into Low Variance Clusters Based on Iterative Feature Scaling

  • Open Access

Imputation Strategies for Clustering Mixed-Type Data with Missing Values

  • Open Access

Editorial: Journal of Classification Vol. 40-1

Premium Partner

    Bildnachweise