loading
Papers

Research.Publish.Connect.

Paper

Authors: Chettan Kumar ; Martin Käppel ; Nicolai Schützenmeier ; Philipp Eisenhuth and Stefan Jablonski

Affiliation: Institute for Computer Science, University of Bayreuth, Universitätsstraße 30, Bayreuth and Germany

ISBN: 978-989-758-377-3

Keyword(s): Machine Learning, Algorithm Recommendation, Data Analysis, Information System.

Related Ontology Subjects/Areas/Topics: Architectural Concepts ; Artificial Intelligence ; Business Analytics ; Computer Vision, Visualization and Computer Graphics ; Data Analytics ; Data Engineering ; Data Management and Quality ; Data Management for Analytics ; General Data Visualization ; Information and Scientific Visualization ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Symbolic Systems

Abstract: 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.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.229.142.175

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Kumar, C.; Käppel, M.; Schützenmeier, N.; Eisenhuth, P. and Jablonski, S. (2019). 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, ISBN 978-989-758-377-3, pages 408-415. DOI: 10.5220/0008117404080415

@conference{data19,
author={Chettan Kumar. and Martin Käppel. and Nicolai Schützenmeier. and Philipp Eisenhuth. and Stefan Jablonski.},
title={A Comparative Study for the Selection of Machine Learning Algorithms based on Descriptive Parameters},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2019},
pages={408-415},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008117404080415},
isbn={978-989-758-377-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - A Comparative Study for the Selection of Machine Learning Algorithms based on Descriptive Parameters
SN - 978-989-758-377-3
AU - Kumar, C.
AU - Käppel, M.
AU - Schützenmeier, N.
AU - Eisenhuth, P.
AU - Jablonski, S.
PY - 2019
SP - 408
EP - 415
DO - 10.5220/0008117404080415

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.