DocumentCode :
1567058
Title :
Pre-processing using Topographic Mappings
Author :
Wu, Ying ; Fyfe, Colin
Author_Institution :
Sch. of Comput., Paisley Univ.
Volume :
3
fYear :
2005
Firstpage :
1881
Lastpage :
1884
Abstract :
We review two recently developed methods which are used to improve classifier accuracy, bagging and the random subspace method. Both of these methods (and other similar methods) may be characterized as deleting some of the information in the training set and creating classifiers which, though themselves sub-optimal, may be combined to create a better classifier than that created using the original data. We pre-process the data using an unsupervised method which creates topographic mappings and show that the resulting classifiers exhibit diversity and better performance
Keywords :
data mining; mean square error methods; self-organising feature maps; unsupervised learning; bagging method; data classification; data mining; mean absolute error; mean squared error; random subspace method; topographic mappings; unsupervised method; Bagging; Boosting; Data mining; Diversity reception; Performance evaluation; Robustness; Silver; Statistics; Technological innovation; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
Type :
conf
DOI :
10.1109/ICNNB.2005.1614992
Filename :
1614992
Link To Document :
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