TY - GEN
T1 - Fusion of pairwise nearest-neighbor classifiers based on pairwise-weighted distance metric and Dempster-Shafer theory
AU - Jiao, Lianmeng
AU - Denoux, Thierry
AU - Pan, Quan
N1 - Publisher Copyright:
© 2014 International Society of Information Fusion.
PY - 2014/10/3
Y1 - 2014/10/3
N2 - The performance of the nearest-neighbor (NN) classifier is known to be very sensitive to the distance metric used in classifying a query pattern, especially in scarce-prototype cases. In this paper, a pairwise-weighted (PW) distance metric related to pairs of class labels is proposed. Compared with the existing distance metrics, it provides more flexibility to design the feature weights so that the local specifics in feature space can be well characterized. Base on the proposed PW distance metric, a polychotomous NN classification problem is solved by combining several pairwise NN (PNN) classifiers within the framework of Dempster-Shafer theory to deal with the uncertain output information. Two experiments based on synthetic and real data sets were carried out to show the effectiveness of the proposed method.
AB - The performance of the nearest-neighbor (NN) classifier is known to be very sensitive to the distance metric used in classifying a query pattern, especially in scarce-prototype cases. In this paper, a pairwise-weighted (PW) distance metric related to pairs of class labels is proposed. Compared with the existing distance metrics, it provides more flexibility to design the feature weights so that the local specifics in feature space can be well characterized. Base on the proposed PW distance metric, a polychotomous NN classification problem is solved by combining several pairwise NN (PNN) classifiers within the framework of Dempster-Shafer theory to deal with the uncertain output information. Two experiments based on synthetic and real data sets were carried out to show the effectiveness of the proposed method.
KW - Dempster-Shafer theory
KW - nearest-neighbor classifier
KW - pairwise-weighted distance metric
KW - pattern classification
UR - https://www.scopus.com/pages/publications/84910599765
M3 - 会议稿件
AN - SCOPUS:84910599765
T3 - FUSION 2014 - 17th International Conference on Information Fusion
BT - FUSION 2014 - 17th International Conference on Information Fusion
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Conference on Information Fusion, FUSION 2014
Y2 - 7 July 2014 through 10 July 2014
ER -