Comparison of Fuzzy Functions for Low Quality Data GAP Algorithms View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Enrique de la Cal , José R. Villar , Marco García-Tamargo , Javier Sedano

ABSTRACT

The undesired effects of data gathered from real world can be produced by the noise in the process, the bias of the sensors and the presence of hysteresis, among other uncertainty sources. Data gathered by this way are called Low Quality Data (LQD). Thus, uncertainty representation tools are needed for using in learning models with this kind of data. This work presents a method to represent the uncertainty and an approach for learning white box Equation Based Models (EBM). The proficiency of the representations with different noise levels and fitness functions typology is compared. The numerical results show that the use of the described objectives improves the proficiency of the algorithms. It has been also proved that each meta-heuristic determines the typology of fitness function. More... »

PAGES

339-349

References to SciGraph publications

Book

TITLE

Hybrid Artificial Intelligent Systems

ISBN

978-3-642-28930-9
978-3-642-28931-6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-28931-6_33

DOI

http://dx.doi.org/10.1007/978-3-642-28931-6_33

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1037485867


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