An ensemble technique to handle missing data from sensors

Automated classification is often used in advanced systems to monitor system events. All data, and hence features from all sensors, must be present in order to make a meaningful classification. An ensemble approach, Learn ⁺⁺.MF, was recently introduced that allows classification with up to 10% of feature missing, where several classifiers are trained on random subsets of the available sensor data. Given an instance with missing features, only those classifiers trained with the available features are then used in classification. In this paper, we present a modified approach that accommodates up to 30% missing features along with the effect of varying algorithm parameters.