The Representation Race - Preprocessing for Handling Time Phenomena

10/26/2020

∙

by   Katharina Morik, et al.

∙

0

∙

share

Designing the representation languages for the input, L E, and output, L H, of a learning algorithm is the hardest task within machine learning applications. This paper emphasizes the importance of constructing an appropriate representation L E for knowledge discovery applications using the example of time related phenomena. Given the same raw data — most frequently a database with time-stamped data — rather different representations have to be produced for the learning methods that handle time. In this paper, a set of learning tasks dealing with time is given together with the input required by learning methods which solve the tasks. Transformations from raw data to the desired representation are illustrated by three case studies.