A New Neural Network to Process Missing Data without Imputation

Authors:
M. Randolph-Gips
Affiliations:
-
Venue:
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Year:
2008

Citing 0
Cited 1

Imputing missing values for mixed numeric and categorical attributes based on incomplete data hierarchical clustering

KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management

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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.