Author:
Matthias Reif
Affiliation:
German Research Center for Artificial Intelligence, Germany
Keyword(s):
Meta-learning, Ranking, Algorithm selection, Dataset, Pattern recognition, Classification.
Related
Ontology
Subjects/Areas/Topics:
Classification
;
Meta Learning
;
Model Selection
;
Pattern Recognition
;
Theory and Methods
Abstract:
New approaches in pattern recognition are typically evaluated against standard datasets, e.g. from UCI or StatLib. Using the same and publicly available datasets increases the comparability and reproducibility of evaluations. In the field of meta-learning, the actual dataset for evaluation is created based on multiple other datasets. Unfortunately, no comprehensive dataset for meta-learning is currently publicly available. In this paper, we present a novel and publicly available dataset for meta-learning based on 83 datasets, six classification
algorithms, and 49 meta-features. Different target variables like accuracy and training time of the classifiers as well as parameter dependent measures are included as ground-truth information. Therefore, the meta-dataset can be used for various meta-learning tasks, e.g. predicting the accuracy and training time of classifiers or predicting the optimal parameter values. Using the presented meta-dataset, a convincing and comparable evaluation o
f new meta-learning approaches is possible.
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