Coupling different methods for overcoming the class imbalance problem

Many classification problems must deal with imbalanced datasets where one class \u2013 the majority class \u2013 outnumbers the other classes. Standard classification methods do not provide accurate predictions in this setting since classification is generally biased towards the majority class. The minority classes are oftentimes the ones of interest (e.g., when they are associated with pathological conditions in patients), so methods for handling imbalanced datasets are critical. Using several different datasets, this paper evaluates the performance of state-of-the-art classification methods for handling the imbalance problem in both binary and multi-class datasets. Different strategies are considered, including the one-class and dimension reduction approaches, as well as their fusions. Moreover, some ensembles of classifiers are tested, in addition to stand-alone classifiers, to assess the effectiveness of ensembles in the presence of imbalance. Finally, a novel ensemble of ensembles is designed specifically to tackle the problem of class imbalance: the proposed ensemble does not need to be tuned separately for each dataset and outperforms all the other tested approaches. To validate our classifiers we resort to the KEEL-dataset repository, whose data partitions (training/test) are publicly available and have already been used in the open literature: as a consequence, it is possible to report a fair comparison among different approaches in the literature. Our best approach (MATLAB code and datasets not easily accessible elsewhere) will be available at https://www.dei.unipd.it/node/2357

Response to Letter Regarding Article, "Is Worsening Renal Function an Ominous Prognostic Sign in Patients With Acute Heart Failure? The Role of Congestion and Its Interaction With Renal Function"

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HELL: High-energy electrons by laser light, a user-oriented experimental platform at ELI beamlines

Laser wake field acceleration (LWFA) is an efficient method to accelerate electron beams to high energy. This is a benefit in research infrastructures where a multidisciplinary environment can benefit from the different secondary sources enabled, having the opportunity to extend the range of applications that is accessible and to develop new ideas for fundamental studies. The ELI Beamline project is oriented to deliver such beams to the scientific community both for applied and fundamental research. The driver laser is a Ti:Sa diode-pumped system , running at a maximum performance of 10 Hz, 30 J, and 30 fs. The possibilities to setup experiments using different focal lengths parabolas, as well as the possibility to counter-propagate a second laser beam intrinsically synchronized, are considered in the electron acceleration program. Here, we review the laser-driven electron acceleration experimental platform under implementation at ELI Beamlines, the HELL (High-energy Electrons by Laser Light) experimental platform