An evidential reasoning based classification algorithm and its application for face recognition with class noise

For classification problems, in practice, real-world data may suffer from two types of noise, attribute noise and class noise. It is the key for improving recognition performance to remove as much of their adverse effects as possible. In this paper, a formalism algorithm is proposed for classification problems with class noise, which is more challenging than those with attribute noise. The proposed formalism algorithm is based on evidential reasoning theory which is a powerful tool to deal with uncertain information in multiple attribute decision analysis and many other areas. Thus, it may be more effective alternative to handle noisy label information. And then a specific algorithm—Evidential Reasoning based Classification algorithm (ERC) is derived to recognize human faces under class noise conditions. The proposed ERC algorithm is extensively evaluated on five publicly available face databases with class noise and yields good performance.

► This paper focuses on applying Evidential Reasoning (ER) to classification. ► Based on ER, a novel formalism classification algorithm (FCA) is proposed. ► Based on FCA, a specific algorithm, ERC, is derived to recognize human faces. ► ERC has shown a good performance under class noise with the help of ER.