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PRESISTANT : data pre-processing assistant

Author
Bilalli, B.; Abello, A.; Aluja, T.; Munir, R.; Wrembel, R.
Type of activity
Presentation of work at congresses
Name of edition
30th International Conference on Advanced Information Systems Engineering
Date of publication
2018
Presentation's date
2018-06-13
Book of congress proceedings
Information Systems in the Big Data Era: CAiSE Forum 2018, Tallinn, Estonia, June 11-15, 2018: proceedings
First page
57
Last page
65
Publisher
Springer
DOI
https://doi.org/10.1007/978-3-319-92901-9 Open in new window
Repository
http://hdl.handle.net/2117/127984 Open in new window
URL
https://link.springer.com/chapter/10.1007%2F978-3-319-92901-9_6 Open in new window
Abstract
A concrete classification algorithm may perform differently on datasets with different characteristics, e.g., it might perform better on a dataset with continuous attributes rather than with categorical attributes, or the other way around. Typically, in order to improve the results, datasets need to be pre-processed. Taking into account all the possible pre-processing operators, there exists a staggeringly large number of alternatives and non-experienced users become overwhelmed. Trial and error...
Citation
Bilalli, B. [et al.]. PRESISTANT : data pre-processing assistant. A: International Conference on Advanced Information Systems Engineering. "Information Systems in the Big Data Era: CAiSE Forum 2018, Tallinn, Estonia, June 11-15, 2018: proceedings". Berlín: Springer, 2019, p. 57-65.
Keywords
Data mining, Data pre-processing, Meta-learning
Group of research
DTIM - Database Technologies and lnformation Management Group
LIAM - Laboratory of Information Analysis and Modelling
inLab FIB
inSSIDE - integrated Software, Service, Information and Data Engineering

Participants