Threshold optimization for classification in imbalanced data in a problem of gamma-ray astronomy

Tobias Voigt (), Roland Fried (), Michael Backes () and Wolfgang Rhode ()

Advances in Data Analysis and Classification, 2014, vol. 8, issue 2, 195-216

Abstract: We introduce a method to minimize the mean square error (MSE) of an estimator which is derived from a classification. The method chooses an optimal discrimination threshold in the outcome of a classification algorithm and deals with the problem of unequal and unknown misclassification costs and class imbalance. The approach is applied to data from the MAGIC experiment in astronomy for choosing an optimal threshold for signal-background-separation. In this application one is interested in estimating the number of signal events in a dataset with very unfavorable signal to background ratio. Minimizing the MSE of the estimation is a rather general approach which can be adapted to various other applications, in which one wants to derive an estimator from a classification. If the classification depends on other or additional parameters than the discrimination threshold, MSE minimization can be used to optimize these parameters as well. We illustrate this by optimizing the parameters of logistic regression, leading to relevant improvements of the current approach used in the MAGIC experiment. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Classification; Thresholding; MAGIC; Imbalanced data; Unknown misclassification costs; Random forest; 62-07; 65Z05; 85-08; 90-08 (search for similar items in EconPapers)
Date: 2014
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/s11634-014-0167-5 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advdac:v:8:y:2014:i:2:p:195-216

Ordering information: This journal article can be ordered from
http://www.springer. ... ds/journal/11634/PS2

DOI: 10.1007/s11634-014-0167-5

Access Statistics for this article

Advances in Data Analysis and Classification is currently edited by H.-H. Bock, W. Gaul, A. Okada, M. Vichi and C. Weihs

More articles in Advances in Data Analysis and Classification from Springer, German Classification Society - Gesellschaft für Klassifikation (GfKl), Japanese Classification Society (JCS), Classification and Data Analysis Group of the Italian Statistical Society (CLADAG), International Federation of Classification Societies (IFCS)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().