Authors:
Benoît Gandar
1
;
Gaëlle Loosli
2
and
Guillaume Deffuant
3
Affiliations:
1
Clermont Université, Université Blaise Pascal and Cemagref de Clermont-Ferrand, France
;
2
Clermont Université, Université Blaise Pascal and CNRS, France
;
3
Cemagref de Clermont-Ferrand, France
Keyword(s):
Machine Learning, Classification, Space Filling Design, Dispersion.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
;
Theory and Methods
Abstract:
Recent theoretical work proposes criteria of dispersion to generate learning points. The aim of this paper is
to convince the reader, with experimental proofs, that dispersion is a good criterion in practice for generating
learning points for classification problems. Problem of generating learning points consists then in generating
points with the lowest dispersion. As a consequence, we present low dispersion algorithms existing in the
literature, analyze them and propose a new algorithm.