On the predictive power of meta-features in OpenML - Bilalli, Besim; Abelló, Alberto; Aluja-Banet, Tomas (Zielona Góra: Uniwersytet Zielonogórski)
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).
Keywords: feature extraction; feature selection; meta-learning