- Title
- A new loss function for robust classification
- Creator
- Zhao, Lei; Mammadov, Musa; Yearwood, John
- Date
- 2014
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160646
- Identifier
- vital:12249
- Identifier
-
https://doi.org/10.3233/IDA-140664
- Identifier
- ISBN:1088-467X
- Abstract
- Loss function plays an important role in data classification. Manyloss functions have been proposed and applied to differentclassification problems. This paper proposes a new so called thesmoothed 0-1 loss function, that could be considered as anapproximation of the classical 0-1 loss function. Due to thenon-convexity property of the proposed loss function, globaloptimization methods are required to solve the correspondingoptimization problems. Together with the proposed loss function, wecompare the performance of several existing loss functions in theclassification of noisy data sets. In this comparison, differentoptimization problems are considered in regards to the convexity andsmoothness of different loss functions. The experimental resultsshow that the proposed smoothed 0-1 loss function works better ondata sets with noisy labels, noisy features, and outliers. © 2014 - IOS Press and the authors. All rights reserved.
- Publisher
- IOS Press
- Relation
- Intelligent Data Analysis Vol. 18, no. 4 (2014), p. 697-715
- Rights
- Copyright © 2014 - IOS Press and the authors. All rights reserved.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 0804 Data Format; 1702 Cognitive Science; Classification; Data mining; Loss function; Machine learning; Optimization
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