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We discuss an ensemble-of-classifiers based algorithm for the missing feature problem. The proposed approach is inspired in part by the random subspace method, and in part by the incremental learning algorithm, Learn++. The premise is to generate an adequately large number of classifiers, each trained on a different and random combination of features, drawn from an iteratively updated distribution. To classify an instance with missing features, only those classifiers whose training data did notdoi:10.1109/ijcnn.2007.4371330 dblp:conf/ijcnn/DePasqualeP07 fatcat:4nyqfy2g7rantfrnuly73fdnqy