DocumentCode :
948216
Title :
Local Feature Weighting in Nearest Prototype Classification
Author :
Fernández, Fernando ; Isasi, Pedro
Author_Institution :
Univ. Carlos III de Madrid, Madrid
Volume :
19
Issue :
1
fYear :
2008
Firstpage :
40
Lastpage :
53
Abstract :
The distance metric is the corner stone of nearest neighbor (NN)-based methods, and therefore, of nearest prototype (NP) algorithms. That is because they classify depending on the similarity of the data. When the data is characterized by a set of features which may contribute to the classification task in different levels, feature weighting or selection is required, sometimes in a local sense. However, local weighting is typically restricted to NN approaches. In this paper, we introduce local feature weighting (LFW) in NP classification. LFW provides each prototype its own weight vector, opposite to typical global weighting methods found in the NP literature, where all the prototypes share the same one. Providing each prototype its own weight vector has a novel effect in the borders of the Voronoi regions generated: They become nonlinear. We have integrated LFW with a previously developed evolutionary nearest prototype classifier (ENPC). The experiments performed both in artificial and real data sets demonstrate that the resulting algorithm that we call LFW in nearest prototype classification (LFW-NPC) avoids overfitting on training data in domains where the features may have different contribution to the classification task in different areas of the feature space. This generalization capability is also reflected in automatically obtaining an accurate and reduced set of prototypes.
Keywords :
geometry; learning (artificial intelligence); pattern classification; Voronoi region; distance metric; evolutionary nearest prototype classifier; local feature weighting; nearest neighbor-based method; nearest prototype algorithm; nearest prototype classification; Evolutionary learning; local feature weighting (LFW); nearest prototype (NP) classification; weighted Euclidean distance; Algorithms; Artificial Intelligence; Cluster Analysis; Discriminant Analysis; Humans; Neural Networks (Computer); Pattern Recognition, Automated;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2007.902955
Filename :
4359199
Link To Document :
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