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This paper analyzes a number of strategies that are devoted to improving the generalization capabilities of neural-network-based soft sensors when only small data sets are available. The aim of this paper is to search for a strategy that is able to cope with the problem of scarcity of experimental data, which often arises in industrial applications. The strategies that are considered are based on the manipulation of experimental training data sets to increase their diversity either by injecting noise into the available data or by using the bootstrap resampling approach. A new method, which is based on an aggregation of neural models, trained on different training data sets, which are obtained by noise injection and bootstrap resampling, is proposed in this paper. The methods considered were compared in an industrial case study regarding the design of a backup soft sensor for a thermal cracking unit, working in a refinery in Sicily, Italy. The results of the case study show that all the methods considered produced an improvement in the estimation capability of the models. The best performance was obtained by using the method proposed by the authors.