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On the predictive power of meta-features in OpenML

Author
Bilalli, B.; Abello, A.; Aluja, T.
Type of activity
Journal article
Journal
International journal of applied mathematics and computer science
Date of publication
2017-12-20
Volume
27
Number
4
First page
697
Last page
712
DOI
https://doi.org/10.1515/amcs-2017-0048 Open in new window
Repository
http://hdl.handle.net/2117/113229 Open in new window
URL
https://www.degruyter.com/view/j/amcs.2017.27.issue-4/amcs-2017-0048/amcs-2017-0048.xml Open in new window
Abstract
The demand for performing data analysis is steadily rising. As a consequence, people of different profiles (i.e., non-experienced users) have started to analyze their data. However, this is challenging for them. A key step that poses difficulties and determines the success of the analysis is data mining (model/algorithm selection problem). Meta-learning is a technique used for assisting non-expert users in this step. The effectiveness of meta-learning is, however, largely dependent on the descri...
Citation
Bilalli, B., Abello, A., Aluja, T. On the predictive power of meta-features in OpenML. "International journal of applied mathematics and computer science", 20 Desembre 2017, vol. 27, núm. 4, p. 697-712.
Keywords
feature extraction, feature selection, meta-learning
Group of research
DTIM - Database Technologies and lnformation Management Group
IMP - Information Modelling and Processing
inLab FIB
inSSIDE - integrated Software, Service, Information and Data Engineering

Participants

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