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.
classification feature selection belief functions uncertain data dempstershafer theory evidence theory