An evidential classifier based on feature selection and two-step classification strategy

• A classifier is based on Belief Functions to tackle uncertain data.
• The classifier composed by feature selection and a two-step classification.
• A new combination rule to better represent data uncertainty.
• A new feature selection is based on minimizing uncertainty with sparse constraint.
• Two-step classification improving accuracy of decision making.

In this paper, we investigate ways to learn efficiently from uncertain data using belief functions. In order to extract more knowledge from imperfect and insufficient information and to improve classification accuracy, we propose a supervised learning method composed of a feature selection procedure and a two-step classification strategy. Using training information, the proposed feature selection procedure automatically determines the most informative feature subset by minimizing an objective function. The proposed two-step classification strategy further improves the decision-making accuracy by using complementary information obtained during the classification process. The performance of the proposed method was evaluated on various synthetic and real datasets. A comparison with other classification methods is also presented.