An evidential classifier based on feature selection and two-step classification strategy
作者：
Highlights：
• 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.
摘要
Highlights•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.
论文关键词：Dempster–Shafer theory,Evidence theory,Belief functions,Uncertain data,Feature selection,Classification,EK-NN,evidence-theoretic k-nearest neighbor classification,CCR,credal classification rule,BBAs,basic belief assignments,ANN,artificial neural networks,CART,classification and regression tree,BK-NN,belief-based k-nearest neighbor classification method
论文评审过程：Received 9 July 2014, Revised 19 January 2015, Accepted 23 January 2015, Available online 2 February 2015.
论文官网地址：https://doi.org/10.1016/j.patcog.2015.01.019
