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Трага: Дома I Циклус Тековни соопштенија за студентите Покането предавање на тема ,,Deep Statistical Comparison on Meta-heuristic Stochastic Optimization Algorithms“

Покането предавање на тема ,,Deep Statistical Comparison on Meta-heuristic Stochastic Optimization Algorithms“

Објавено на 04.05.2018 година

Факултетот за електротехника и информациски технологии (ФЕИТ) и Факултетот за информатички науки и компјутерско инженерство (ФИНКИ), заедно со одделот за компјутери и одделот за теорија на информации при македонската секција на IEEE, на 11.05.2018 со почеток во 10:00 часот, во салата за состаноци на ФЕИТ, организираат покането предавање на тема:

,,Deep Statistical Comparison on Meta-heuristic Stochastic Optimization Algorithms

Предавач: Tome Eftimov, PhD, Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia

Апстракт: To determine the strengths and weaknesses of a selected algorithm, its performance should be compared with performances of state-of-the-art algorithms. The idea behind those comparisons is that by using the results obtained on different problems (e.g., functions, data sets), the "best" algorithm (i.e. algorithm that perform best in average over all problems) can be found, or to use the benchmarking results to transfer the knowledge onto a real-world problem. Statistical analyses that are performed in such cases are crucial and need to be made with a great care because they provide the information from where the conclusions are made, so an appropriate statistical analysis should be performed. Nowadays, many researchers have problems in selecting the right statistic that will be applied on a selected performance measure. Additionally, applying the appropriate statistical test requires knowledge of the necessary conditions about the data that must be met in order to apply it. This kind of misunderstanding is all too common in the research community and can be observed in many high-ranking journal papers. For these reasons, we proposed a novel approach for statistical comparison, known as Deep Statistical Comparison, which provides more robust statistical results than previous state-of-the-art approaches when results are affected by outliers or statistical insignificant difference that could exist between data values. An actual demonstration in which the audience will get familiar with a web-based tool (http://ws.ijs.si/dsc/), designed to make a deep statistical comparison easier, will be given at the end of the talk.


The evaluation of the DSC approach is performed by using the results from the Black-Box Benchmarking 2015, which is a competition that provides single-objective functions for benchmarking.

Биографија: Tome Eftimov is a researcher at Computer Systems Department, Jožef Stefan Institute, in Ljubljana, Slovenia. In 2011, he obtained his bachelor degree from the Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, Macedonia, and in 2013, he received master degree from the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Macedonia. In 2018, he received his PhD degree in Information and Communication Technologies at the Jožef Stefan International Postgraduate School, Ljubljana, Slovenia. His areas of research include statistical data analysis, stochastic optimization algorithms, natural language processing, machine learning, and information theory. His recently published work on Deep Statistical Comparison in top-tier journal is selected as Hot Off the Press talk (i.e. one of the best papers related to stochastic optimization published in 2017 in the world), which will be presented at the Genetic and Evolutionary Computation Conference (i.e. GECCO 2018) in Kyoto, Japan.



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