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
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;
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
DOI :
10.1109/ICMLA.2008.89
