he aspect of using complex classifiers or ensembles of classifiers becomes extremely important due to the fact that increasingly we deal with difficult, multidimensional, and large data. Ensemble methods can generalize better and reduce the overfitting in comparison with a single classifier. Moreover, in the case of classifiers that perform local searches, the use of multiple classifiers often brings very good results. Using complex classifiers with hierarchical, parallel, or otherwise organized structures we can also speed up the computations with the help of parallel processing. Sometimes the improvement of classification time and quality can be obtained by searching only subspaces of possible solutions.

The main goal of the session is to attract researchers who will present their contributions, interdisciplinary approaches, or case studies in the area of complex and dispersed classifiers. Submissions are expected from, but not limited to the following topics: ensembles of classifiers, complex rule-based systems, data analytics, data mining using difficult data, as well as various aspects of machine learning, big data, and parallel processing.

Paper Submission
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All papers must be written in standard English. The maximum page limit is 10 inclusive of figures and tables. Submissions need to be submitted in pdf format through the through the PROSE online submission and review system.

Co-Chairs
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Małgorzata Przybyła-Kasperek, University of Silesia in Katowice, Poland
E-mail: malgorzata.przybyla-kasperek@us.edu.pl
Rafał Skinderowicz, University of Silesia in Katowice, Poland
E-mail: rafal.skinderowicz@us.edu.pl