Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Visual Guidance to Find the Right Spot in Parameter Space

: Brakowski, Alexander; Maier, Sebastian; Kuijper, Arjan


Yamamoto, Sakae (Ed.); Mori, Hirohiko (Ed.):
Human Interface and the Management of Information. Interaction, Visualization, and Analytics : 20th International Conference, HIMI 2018, Held as Part of HCI International 2018. Proceedings, Part I
Cham: Springer International Publishing, 2018 (Lecture Notes in Computer Science 10904)
ISBN: 978-3-319-92042-9 (print)
ISBN: 978-3-319-92043-6 (online)
International Conference on Human Interface and the Management of Information (HIMI) <20, 2018, Las Vegas/Nev.>
International Conference on Human-Computer Interaction (HCII) <20, 2018, Las Vegas/Nev.>
Fraunhofer IGD ()
Big data; Visualization; Parameterization; Filtering; Digitized Work; Human computer interaction (HCI)

The last few decades brought upon a technological revolution that has been generating data by users with an ever increasing variety of digital devices, resulting in such an incredible volume of data, that we are unable to make any sense of it any more. One solution to decrease the required execution time of these algorithms would be the preprocessing of the data by sampling it before starting the exploration process. That indeed does help, but one issue remains when using the available Machine Learning and Data Mining algorithms: they all have parameters. That is a big problem for most users, because a lot of these parameters require expert knowledge to be able to tune them. Even for expert users a lot of the parameter configurations highly depend on the data. In this work we will present a system that tackles that data exploration process from the angle of parameter space exploration. Here we use the active learning approach and iteratively try to query the user for their opinion of an algorithm execution. For that an end-user only has to express a preference for algorithm results presented to them in form of a visualisations. That way the system is iteratively learning the interest of the end-user, which results in good parameters at the end of the process. A good parametrisation is obviously very subjective here and only reflects the interest of an user. This solution has the nice ancillary property of omitting the requirement of expert knowledge when trying to explore an data set with Data Mining or Machine Learning algorithms. Optimally the end-user does not even know what kind of parameters the algorithms require.