DocumentCode
1919397
Title
Discrete feature weighting & selection algorithm
Author
Jankowski, Norbert
Author_Institution
Dept. of Informatics, Nicholas Copernicus Univ., Torun, Poland
Volume
1
fYear
2003
fDate
20-24 July 2003
Firstpage
636
Abstract
A new method of feature weighting, useful also for feature selection has been described. It is quite efficient and gives quite accurate results. In general weighting algorithm may be used with any kind of learning algorithm. The weighting algorithm with k-nearest neighbors model was used to estimate the optimal feature base for a given distance measure. Results obtained with this algorithm clearly show its superior performance in several benchmark tests.
Keywords
learning (artificial intelligence); pattern classification; set theory; vectors; benchmark tests; discrete feature weighting algorithm; k-nearest neighbors model; learning algorithm; optimal feature estimation; selection algorithm; Equations; Heart; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-7898-9
Type
conf
DOI
10.1109/IJCNN.2003.1223438
Filename
1223438
Link To Document