Digital Library of Zielona Góra contains 9 272 digital objects
Object
Title: On the predictive power of meta-features in OpenML
Contributor:
Stefanowski, Jerzy - ed. ; Krawiec, Krzysztof - ed. ; Wrembel, Robert - ed.
Subtitle:
Group publication title:
Abstract:
The demand for performing data analysis is steadily rising. As a consequence, people of different profiles (i.e., nonexperienced 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 description/characterization of datasets (i.e., meta-features used for meta-learning). ; There is a need for improving the effectiveness of meta-learning by identifying and designing more predictive meta-features. In this work, we use a method from exploratory factor analysis to study the predictive power of different meta-features collected in OpenML, which is a collaborative machine learning platform that is designed to store and organize meta-data about datasets, data mining algorithms, models and their evaluations. We first use the method to extract latent features, which are abstract concepts that group together meta-features with common characteristics. ; Then, we study and visualize the relationship of the latent features with three different performance measures of four classification algorithms on hundreds of datasets available in OpenML, and we select the latent features with the highest predictive power. Finally, we use the selected latent features to perform meta-learning and we show that our method improves the meta-learning process. Furthermore, we design an easy to use application for retrieving different meta-data from OpenML as the biggest source of data in this domain.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Resource Identifier:
DOI:
Pages:
Source:
AMCS, volume 27, number 4 (2017) ; click here to follow the link
Language:
License CC BY 4.0:
Rights:
Biblioteka Uniwersytetu Zielonogórskiego
Object collections:
- Repository > Faculties > Faculty of Computer, Electrical and Control Engineering
- Repository > Types of work > Articles
- Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (2017)
Last modified:
Jul 14, 2025
In our library since:
Jul 7, 2025
Number of object content hits:
42
All available object's versions:
https://zbc.uz.zgora.pl/repozytorium/publication/100710
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 27, number 1 (2017)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 27, number 2 (2017)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 27, number 3 (2017)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 27, number 4 (2017)
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| Edition name | Date |
|---|---|
| On the predictive power of meta-features in OpenML | Jul 14, 2025 |
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