DocumentCode :
2541158
Title :
Modeling Gestalt laws for classification
Author :
Wen, Guihua ; Pan, Xingjiang ; Jiang, Lijun ; Wen, Jun
Author_Institution :
South China Univ. of Technol., Guangzhou, China
fYear :
2010
fDate :
7-9 July 2010
Firstpage :
914
Lastpage :
918
Abstract :
The k-nearest neighbors classifier is simple and often results in good classification performance on problems with unknown and non-normal distributions. However, its selected nearest neighbors on noisy, sparse, or imbalanced data are often inconsistent with our intuition and in turn leads to the worse performance. This paper applies Gestalt visual perceptual laws to design a new KNN classifie r. It applies the neighborhood relation between any two data points to construct the geometry shape of data and then applies the Gestalt laws on this shape to perform the classification. The conducted experiments on challenging benchmark real data validate the proposed approach.
Keywords :
computational geometry; learning (artificial intelligence); pattern classification; visual perception; Gestalt visual perceptual laws; KNN classifier; classification; geometry shape construction; imbalanced data; k-nearest neighbors classifier; neighborhood relation; nonnormal distributions; real data validation; Accuracy; Nearest neighbor searches; Robustness; Shape; Strontium; Training; Visualization; Classification; Gestalt Laws; cognitive geom-etry; nearest neighbors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cognitive Informatics (ICCI), 2010 9th IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-8041-8
Type :
conf
DOI :
10.1109/COGINF.2010.5599779
Filename :
5599779
Link To Document :
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