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
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.