Dataset of financial ratios of Slovak companies

Dataset consists of financial ratios up to three years prior evaluation year for firms divided into four industries (retail, agriculture, manufacture, construction) for four evaluation years (2013, 2014, 2015, 2016)

Dataset of financial ratios of Slovak companies

Dataset consists of financial ratios up to three years prior evaluation year for firms divided into four industries (retail, agriculture, manufacture, construction) for four evaluation years (2013, 2014, 2015, 2016)

Dataset of financial ratios of Slovak companies

Dataset consists of financial ratios up to three years prior evaluation year for firms divided into four industries (retail, agriculture, manufacture, construction) for four evaluation years (2013, 2014, 2015, 2016)

Performance-Driven Handwriting Task Selection for Parkinson's Disease Classification

Diagnosing and monitoring Parkinson’s disease (PD) is a topic of current research in many fields, including AI. The innovative challenge is to develop a low-cost, non-invasive tool to support clinicians at the point of care. In particular, since handwriting difficulties in PD patients are well-known, changes in handwriting have emerged as a powerful discriminant factor for PD assessment. A crucial step in designing a decision support system based on handwriting concerns the choice of the most appropriate handwriting tasks to be administered for data acquisition. When data are collected, traditional approaches assume that different tasks, although not with the same impact, are all important for classification. However, not all tasks are likely to be useful for diagnosis, and the inclusion of these tasks may be detrimental to prediction accuracy. This work investigates the potential of an optimal subset of tasks for a more accurate PD classification. The evaluation is carried out by adopting a performance-driven multi-expert approach on different handwriting tasks performed by the same subjects. The multi-expert system is based on similar or conceptually different classifiers trained on features related to the dynamics of the handwriting process. The proposed approach improves baseline results on the PaHaW data set

Evaluating feature selection robustness on high-dimensional data

With the explosive growth of high-dimensional data, feature selection has become a crucial step of machine learning tasks. Though most of the available works focus on devising selection strategies that are effective in identifying small subsets of predictive features, recent research has also highlighted the importance of investigating the robustness of the selection process with respect to sample variation. In presence of a high number of features, indeed, the selection outcome can be very sensitive to any perturbations in the set of training records, which limits the interpretability of the results and their subsequent exploitation in real-world applications. This study aims to provide more insight about this critical issue by analysing the robustness of some state-of-the-art selection methods, for different levels of data perturbation and different cardinalities of the selected feature subsets. Furthermore, we explore the extent to which the adoption of an ensemble selection strategy can make these algorithms more robust, without compromising their predictive performance. The results on five high-dimensional datasets, which are representatives of different domains, are presented and discussed