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A New Loss Function For Robust Classification
Journal article   Open access   Peer reviewed

A New Loss Function For Robust Classification

Lei Zhao, Musa Mammadov and John Yearwood
Intelligent Data Analysis, Vol.18, pp.697-715
2014
DOI: https://doi.org/10.3233/IDA-140664
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https://doi.org/10.3233/IDA-140664View
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Abstract

classification loss function machine learning optimization data mining
Loss function plays an important role in data classification. Many loss functions have been proposed and applied to different classification problems. This paper proposes a new so called the smoothed 0-1 loss function, that could be considered as an approximation of the classical 0-1 loss function. Due to the non-convexity property of the proposed loss function, global optimization methods are required to solve the corresponding optimization problems. Together with the proposed loss function, we compare the performance of several existing loss functions in the classification of noisy data sets. In this comparison, different optimization problems are considered in regards to the convexity and smoothness of different loss functions. The experimental results show that the proposed smoothed 0-1 loss function works better on data sets with noisy labels, noisy features, and outliers.

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Collaboration types
Domestic collaboration
Web Of Science research areas
Computer Science, Artificial Intelligence
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