at Google ScholarTitle data
Kumar, Chettan ; Käppel, Martin ; Schützenmeier, Nicolai ; Eisenhuth, Philipp ; Jablonski, Stefan:
A Comparative Study for the Selection of Machine Learning Algorithms based on Descriptive Parameters.
In:
Proceedings of the 8th International Conference on Data Science, Technology and Applications. Volume 1. DATA. -
Prag
,
2019
. - pp. 408-415
ISBN 978-989-758-377-3
DOI: https://doi.org/10.5220/0008117404080415
Abstract in another language
In this paper, we present a new cheat sheet based approach to select an adequate machine learning algorithm.
However, we extend existing cheat sheet approaches at two ends. We incorporate two different perspectives
towards the machine learning problem while simultaneously increasing the number of parameters decisively.
For each family of machine learning algorithms (e.g. regression, classification, clustering, and association
learning) we identify individual parameters that describe the machine learning problem accurately. We arrange
those parameters in a table and assess known machine learning algorithms in such a table. Our cheat sheet is
implemented as a web application based on the information of the presented tables.
Further data
| Item Type: | Article in a book |
|---|---|
| Refereed: | Yes |
| Keywords: | Machine Learning; Algorithm Recommendation; Data Analysis; Information System |
| Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV > Chair Applied Computer Science IV - Univ.-Prof. Dr.-Ing. Stefan Jablonski Faculties Faculties > Faculty of Mathematics, Physics und Computer Science Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV |
| Result of work at the UBT: | Yes |
| DDC Subjects: | 000 Computer Science, information, general works > 004 Computer science |
| Date Deposited: | 14 Aug 2019 05:50 |
| Last Modified: | 08 Jul 2022 07:39 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/51838 |