Abstract
In multi-label classification tasks, instances are simultaneously associated with multiple labels, representing different and, possibly, related concepts from a domain. One characteristic of these tasks is a high class-label imbalance. In order to obtain improved predictive models, several algorithms either have explored the label dependencies or have dealt with the problem of imbalanced labels. This work proposes a label expansion approach which combines both alternatives. For such, some labels are expanded with data from a related class label, making the labels more balanced and representative. Preliminary experiments show the effectiveness of this approach to improve the Binary Relevance strategy. Particularly, it reduced the number of labels that were never predicted in the test instances. Although the results are preliminary, they are potentially attractive, considering the scale and consistency of the improvement obtained, as well as the broad scope of the proposed approach.

Abstract
Analytic approaches to combine interpretable models, although presented in different contexts, can be generalized to highlight the components that can be specialized. We propose a framework that structures the combination process, formalizes the problems that can be solved in alternative ways and evaluates the combined models based on their predictive ability to replace the base ones, without loss of interpretability. The framework is illustrated with a case study using data from the University of Porto, Portugal, where experiments were carried out. The results show that grouping base models by scientific areas, ordering by the number of variables and intersecting their underlying rules creates conditions for the combined models to outperform them. © 2019, Springer Nature Switzerland AG.

Abstract
Knowledge generalization of ruled-based models, such as decision trees or decision rules, have emerged from different backgrounds. This particular kind of models, given their interpretability, offer several possibilities to be combined. Despite each distinct context, common patterns have emerged revealing the systemic nature of the problem. In this paper, we look at the problem of generalizing the knowledge contained in a set of models as a process formalizing the operations that can be addressed in alternative ways. We also include a set-up to evaluate gen-eralized models based on their ability to replace the base ones from a predictive performance perspective, without loss of interpretability.

Abstract

Abstract

Abstract