DocumentCode
2008524
Title
A New Neural Network to Process Missing Data without Imputation
Author
Randolph-Gips, M.
Author_Institution
Univ. of Houston-Clear Lake, Clear Lake, CA
fYear
2008
fDate
11-13 Dec. 2008
Firstpage
756
Lastpage
762
Abstract
This paper introduces the cosine neural network (COSNN) and shows how it can be used to process data with missing components without imputation. It uses a cosine basis function with a weighted norm which can be trained to match the input data, or it can be set to zero to ´ignore´ missing data components. The COSNN is compared to feedforward neural networks using deletion and imputation. The COSNN is shown to be superior in both a function approximation and a classification test set.
Keywords
data handling; neural nets; COSNN; classification test set; cosine neural network; data processing; feedforward neural networks; function approximation; Feedforward neural networks; Function approximation; Impedance matching; Lakes; Machine learning; Maximum likelihood estimation; Medical tests; Neural networks; Statistical analysis; Testing; Imputation; classification; function approximation; incomplete data; missing values; neural networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference on
Conference_Location
San Diego, CA
Print_ISBN
978-0-7695-3495-4
Type
conf
DOI
10.1109/ICMLA.2008.89
Filename
4725061
Link To Document