Fuzzy Artmap and Neural Network Approach to Online Processing of Inputs
  with Missing Values
  An ensemble based approach for dealing with missing data, without predicting
or imputing the missing values is proposed. This technique is suitable for
online operations of neural networks and as a result, is used for online
condition monitoring. The proposed technique is tested in both classification
and regression problems. An ensemble of Fuzzy-ARTMAPs is used for
classification whereas an ensemble of multi-layer perceptrons is used for the
regression problem. Results obtained using this ensemble-based technique are
compared to those obtained using a combination of auto-associative neural
networks and genetic algorithms and findings show that this method can perform
up to 9% better in regression problems. Another advantage of the proposed
technique is that it eliminates the need for finding the best estimate of the
data, and hence, saves time.
