Abstract
Collaborative representation has been successfully used in pattern recognition and machine learning. However, most existing collaborative representation classification methods are to achieve the highest classification accuracy, assuming the same losses for different misclassifications. This assumption may be ineffective in many real-word applications, as misclassification of different types could lead to different losses. Meanwhile, the class distribution of data is highly imbalanced in real-world applications. To address these problems, Cost-sensitive Collaborative Representation based Classification via Probability Estimation Addressing the Class Imbalance Problem method was proposed. The class label of test samples was predict by minimizing the misclassification losses which are obtained via computing the posterior probabilities. In this paper, a Gaussian function was defined as a probability distribution of collaborative representation coefficient vector and it was transformed into collaborative representation framework via logarithmic operator. The experiments on UCI and YaleB databases show that our method performs competitively compared with other methods.
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Acknowledgements
The authors want to thank the anonymous reviewers and the associate editor for helpful comments and suggestions. This work is supported by the National Natural Science Foundation of China (Grant Nos. 61562013, 21365008 and 61320106008), Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics (No. LD16096x), the Center for Collaborative Innovation in the Technology of IOT and the Industrialization (WLW20060610).
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Liu, Z. et al. (2018). Cost-Sensitive Collaborative Representation Based Classification via Probability Estimation Addressing the Class Imbalance Problem. In: Lu, H., Xu, X. (eds) Artificial Intelligence and Robotics. Studies in Computational Intelligence, vol 752. Springer, Cham. https://doi.org/10.1007/978-3-319-69877-9_31
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DOI: https://doi.org/10.1007/978-3-319-69877-9_31
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