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The notion of Industry 4.0 is having a catalyzing effect for the integration of diverse new technologies towards a new generation of more efficient, agile, and sustainable industrial systems. From our analysis, collaboration issues are at the heart of most challenges of this movement. Therefore, an analysis of collaboration needs to be made at all dimensions of Industry 4.0 vision, complemented with a mapping of these needs to the existing results from the collaborative networks area. In addition to such mapping, some new research challenges for the collaborative networks community, as induced by Industry 4.0, are also identified.

We consider semi-supervised learning, learning task from both labeled and unlabeled instances and in particular, self-training with decision tree learners as base learners. We show that standard decision tree learning as the base learner cannot be effective in a self-training algorithm to semi-supervised learning. The main reason is that the basic decision tree learner does not produce reliable probability estimation to its predictions. Therefore, it cannot be a proper selection criterion in self-training. We consider the effect of several modifications to the basic decision tree learner that produce better probability estimation than using the distributions at the leaves of the tree. We show that these modifications do not produce better performance when used on the labeled data only, but they do benefit more from the unlabeled data in self-training. The modifications that we consider are Naive Bayes Tree, a combination of No-pruning and Laplace correction, grafting, and using a distance-based measure. We then extend this improvement to algorithms for ensembles of decision trees and we show that the ensemble learner gives an extra improvement over the adapted decision tree learners.

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