@article {264,
	title = {Human migration: the big data perspective},
	journal = {International Journal of Data Science and Analytics},
	year = {2020},
	pages = {1{\textendash}20},
	abstract = {How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.},
	author = {Alina Sirbu and Andrienko, Gennady and Andrienko, Natalia and Boldrini, Chiara and Conti, Marco and Giannotti, Fosca and Guidotti, Riccardo and Bertoli, Simone and Jisu Kim and Muntean, Cristina Ioana and others}
}
@conference {254,
	title = {Self-Adapting Trajectory Segmentation},
	booktitle = {3rd International Workshop on Big Mobility Data Analytics (BMDA)  2020},
	year = {2020},
	abstract = {Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, parameter-free and user-adaptive solution that flexibly adjusts the segmentation criteria to the specific user under study. Experiments over real data and comparison against simple competitors show that the flexibility of the proposed method has a positive impact on results.},
	keywords = {Mobility Data Mining, Segmentation, User Modeling},
	author = {Agnese Bonavita and Guidotti, Riccardo and Nanni,Mirco}
}
@conference {241,
	title = {Explaining multi-label black-box classifiers for health applications},
	booktitle = {International Workshop on Health Intelligence},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Today the state-of-the-art performance in classification is achieved by the so-called {\textquotedblleft}black boxes{\textquotedblright}, i.e. decision-making systems whose internal logic is obscure. Such models could revolutionize the health-care system, however their deployment in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of transparency. The typical classification problem in health-care requires a multi-label approach since the possible labels are not mutually exclusive, e.g. diagnoses. We propose MARLENA, a model-agnostic method which explains multi-label black box decisions. MARLENA explains an individual decision in three steps. First, it generates a synthetic neighborhood around the instance to be explained using a strategy suitable for multi-label decisions. It then learns a decision tree on such neighborhood and finally derives from it a decision rule that explains the black box decision. Our experiments show that MARLENA performs well in terms of mimicking the black box behavior while gaining at the same time a notable amount of interpretability through compact decision rules, i.e. rules with limited length.},
	keywords = {Explainable Machine Learning, Healthcare},
	doi = {10.1007/978-3-030-24409-5_9},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-24409-5_9},
	author = {Cecilia Panigutti and Guidotti, Riccardo and Monreale, Anna and Pedreschi, Dino}
}
