  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.
