Title :
Discrete feature weighting & selection algorithm
Author :
Jankowski, Norbert
Author_Institution :
Dept. of Informatics, Nicholas Copernicus Univ., Torun, Poland
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;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223438
