Cost-Sensitive Collaborative Representation Based Classification via Probability Estimation Addressing the Class Imbalance Problem

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.