Abstract
It is recognised that the imbalanced data problem is aggravated by other difficulty factors, such as class overlap. Over the years, several research works have focused on this problematic, although presenting two major hitches: the limitation of test domains and the lack of a formulation of the overlap degree, which makes results hard to generalise. This work studies the performance degradation of classifiers with distinct learning biases in overlap and imbalanced contexts, focusing on the characteristics of the test domains (shape, dimensionality and imbalance ratio) and on to what extent our proposed overlapping measure (degOver) is aligned with the performance results observed. Our results show that MLP and CART classifiers are the most robust to high levels of class overlap, even for complex domains, and that KNN and linear SVM are the most aligned with degOver. Furthermore, we found that the dimensionality of data also plays an important role in explaining performance results.

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.

Abstract
Rural tourism can be an opportunity to perform the development of the most disadvantaged rural areas. In this pursuit, there are many challenges to face for make this sector competitive and economically viable. This paper focuses on develop a better understanding of rural tourism and the need for cooperative paradigms that can leverage its competitiveness. In this context, a conceptual model and a technology-based system is presented to bridge the gap between heritage resources and business opportunities to enable regional development.

Abstract

Abstract
The development of relationships by private non-profit organizations (NPOs) with companies is fundamental to their sustainability. However, relational marketing and the definition of fundraising and communication strategies tend to be under-explored, resulting in punctual relationships with donors. This paper proposes to explore ways of developing relationships between NPOs and companies. After a systematization of the main contributions present in the literature, a qualitative and exploratory study was presented, in which semi-structured interviews were conducted with 8 NPO managers with different fields of activity and 16 managers of small, medium and large companies, all with headquarters in the district of Aveiro. The results show that what drives companies to support NPOs are essentially non-material benefits, such as the emotional impact on employees and other stakeholders. There are clear differences between small and large companies, the latter being more prone to formal relations. It was verified, however, that even the occasional supports can be recurrent and repeated, lacking the capacity to protocol the relation. Overall, the data offer interesting clues to NPO staff, namely resulting from a comparison of the different perspectives of the participants.

Abstract
The integration of renewable generation and electric vehicles (EVs) into smart grids poses an additional challenge to the stochastic energy resource management problem due to the uncertainty related to weather forecast and EVs user-behavior. Moreover, when electricity markets are considered, market price variations cannot be disregarded. In this paper, a two-stage stochastic programming approach to schedule the day-ahead operation of energy resources in smart grids under uncertainty is presented. A realistic case study is performed using a large-scale scenario with nearly 4 million variables with the goal to minimize expected operation cost of energy aggregators. Three scenarios are analyzed to understand the effect of market transactions and external suppliers on the aggregator model. The results suggest that the market transactions can reduce expected cost, while the external supplier offers risk-free price. In addition, the performance metric shows the superiority of the stochastic approach over an equivalent deterministic model. © 2018 Power Systems Computation Conference.