A COMPREHENSIVE DATASET FOR EVALUATING APPROACHES OF VARIOUS META-LEARNING TASKS

Matthias Reif

2012

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 of new meta-learning approaches is possible.

Paper Citation

in Harvard Style

Reif M. (2012). A COMPREHENSIVE DATASET FOR EVALUATING APPROACHES OF VARIOUS META-LEARNING TASKS . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8425-98-0, pages 273-276. DOI: 10.5220/0003736302730276

in Bibtex Style

@conference{icpram12,
author={Matthias Reif},
title={A COMPREHENSIVE DATASET FOR EVALUATING APPROACHES OF VARIOUS META-LEARNING TASKS},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2012},
pages={273-276},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003736302730276},
isbn={978-989-8425-98-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A COMPREHENSIVE DATASET FOR EVALUATING APPROACHES OF VARIOUS META-LEARNING TASKS
SN - 978-989-8425-98-0
AU - Reif M.
PY - 2012
SP - 273
EP - 276
DO - 10.5220/0003736302730276