Extending DEAP with Active Sampling for Evolutionary Supervised Learning

Sana Ben Hamida, Ghita Benjelloun

2021

Abstract

Complexity, variety and large sizes of data bases make the Knowledge extraction a difficult task for supervised machine learning techniques. It is important to provide these techniques additional tools to improve their efficiency when dealing with such data. A promising strategy is to reduce the size of the training sample seen by the learner and to change it regularly along the learning process. Such strategy known as active learning, is suitable for iterative learning algorithms such as Evolutionary Algorithms. This paper presents some sampling techniques for active learning and how they can be applied in a hierarchical way. Then, it details how these techniques could be implemented into DEAP, a Python framework for Evolutionary Algorithms. A comparative study demonstrates how active learning improve the evolutionary learning on two data bases for detecting pulsars and occupancy in buildings.

Paper Citation

in Harvard Style

Ben Hamida S. and Benjelloun G. (2021). Extending DEAP with Active Sampling for Evolutionary Supervised Learning. In Proceedings of the 16th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-523-4, pages 574-582. DOI: 10.5220/0010604605740582

in Bibtex Style

@conference{icsoft21,
author={Sana Ben Hamida and Ghita Benjelloun},
title={Extending DEAP with Active Sampling for Evolutionary Supervised Learning},
booktitle={Proceedings of the 16th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2021},
pages={574-582},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010604605740582},
isbn={978-989-758-523-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - Extending DEAP with Active Sampling for Evolutionary Supervised Learning
SN - 978-989-758-523-4
AU - Ben Hamida S.
AU - Benjelloun G.
PY - 2021
SP - 574
EP - 582
DO - 10.5220/0010604605740582