A multiple classifier fusion approach based on evidence combination is proposed in this paper. The individual classifier is designed based on a refined Nearest Feature Line (NFL),which is called Center-based Nearest N...A multiple classifier fusion approach based on evidence combination is proposed in this paper. The individual classifier is designed based on a refined Nearest Feature Line (NFL),which is called Center-based Nearest Neighbor (CNN). CNN retains the advantages of NFL while it has relatively low computational cost. Different member classifiers are trained based on different feature spaces respectively. Corresponding mass functions can be generated based on proposed mass function determination approach. The classification decision can be made based on the combined evidence and better classification performance can be expected. Experimental results on face recognition provided verify that the new approach is rational and effective.展开更多
A novel classification approach called modified center-based feature line(MCFL)is proposed to reduce the computational cost of the nearest feature line(NFL)and maintain the advantages of NFL.Unlike NFL,MCFL defines a ...A novel classification approach called modified center-based feature line(MCFL)is proposed to reduce the computational cost of the nearest feature line(NFL)and maintain the advantages of NFL.Unlike NFL,MCFL defines a different type of feature line and utilizes both the query point’s local information and corresponding class-global information in training set.In experiments provided,the comparisons with the nearest neighbor(NN),NFL,and other NFL-refined approaches show that the computation time of MCFL can be shortened dramatically with less accuracy decreases.MCFL proposed is probably a better choice for the classification application tasks of large-scale dataset.展开更多
基金Supported by Grant for State Key Program for Basic Research of China (973) (No. 2007CB311006)
文摘A multiple classifier fusion approach based on evidence combination is proposed in this paper. The individual classifier is designed based on a refined Nearest Feature Line (NFL),which is called Center-based Nearest Neighbor (CNN). CNN retains the advantages of NFL while it has relatively low computational cost. Different member classifiers are trained based on different feature spaces respectively. Corresponding mass functions can be generated based on proposed mass function determination approach. The classification decision can be made based on the combined evidence and better classification performance can be expected. Experimental results on face recognition provided verify that the new approach is rational and effective.
基金This work was supported by the State Key Development Program for Basic Research of China(No.2007CB311006).
文摘A novel classification approach called modified center-based feature line(MCFL)is proposed to reduce the computational cost of the nearest feature line(NFL)and maintain the advantages of NFL.Unlike NFL,MCFL defines a different type of feature line and utilizes both the query point’s local information and corresponding class-global information in training set.In experiments provided,the comparisons with the nearest neighbor(NN),NFL,and other NFL-refined approaches show that the computation time of MCFL can be shortened dramatically with less accuracy decreases.MCFL proposed is probably a better choice for the classification application tasks of large-scale dataset.
