… Unfortunately, despite being used in many studies, meta-features are not uniformly … used
in meta-learning. Moreover, it presents an extensive list of meta-features and characterization …

… in meta-learning is how to find appropriate meta-features that … Various strategies for
defining these meta-features have … meta-features that we considered suitable for meta-learning …

… We address this task by using a meta-learning approach … instances (ie, the meta-features)
with the relative performance (ie, … new meta-features for TSP to be applied to the meta-learning …

… (1996)), although definitions of relevant meta-features have been obtained from this work. …
-learners has produced novel meta-features; these meta-features are useful to understand …

… Second, we propose a novel auxiliary meta-learning task with … a meta-learning task, we
show that the meta-features of Dataset2Vec outperform the expert engineered meta-features and …

… After considering the results form extensive empirical study regarding the performance of
various meta-learning methods on unified groups of meta-features, measured by …

… of meta-features (ie dataset characteristics). This is where we can make full use of the
flexibility of the meta-learning … sets of meta-features which will give rise to different meta-learning …

… Meta learning is one of the approaches that acquired knowledge based on the past experience.
The knowledge in Meta-Learning … of candidate algorithms on Meta Features. Based on …

… Instead of manually defining meta-features, we can also learn a joint representation for
groups of tasks. One approach is to build meta-models that generate a landmark-like meta-…

… selection by using a set of meta-features. In this paper, a meta-learning system is developed
using exogenous weather variables as meta-features. The proposed system selects the …