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Authors: Antonio Martínez-Rojas ; Andrés Jiménez-Ramírez and Jose G. Enríquez

Affiliation: Departamento de Lenguajes y Sistemas Informáticos, Universidad de Sevilla, Seville and Spain

Keyword(s): Machine Learning, Automated Machine Learning, Knowledge Representation, Model-Driven Engineering.

Abstract: Nowadays, Machine Learning (ML) algorithms are being widely applied in virtually all possible scenarios. However, developing a ML project entails the effort of many ML experts who have to select and configure the appropriate algorithm to process the data to learn from, between other things. Since there exist thousands of algorithms, it becomes a time-consuming and challenging task. To this end, recently, AutoML emerged to provide mechanisms to automate parts of this process. However, most of the efforts focus on applying brute force procedures to try different algorithms or configuration and select the one which gives better results. To make a smarter and more efficient selection, a repository of knowledge is necessary. To this end, this paper proposes (1) an approach towards a common language to consolidate the current distributed knowledge sources related the algorithm selection in ML, and (2) a method to join the knowledge gathered through this language in a unified store that can be exploited later on. The preliminary evaluations of this approach allow to create a unified store collecting the knowledge of 13 different sources and to identify a bunch of research lines to conduct. (More)

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Paper citation in several formats:
Martínez-Rojas, A.; Jiménez-Ramírez, A. and Enríquez, J. (2019). Towards a Unified Model Representation of Machine Learning Knowledge. In Proceedings of the 15th International Conference on Web Information Systems and Technologies - APMDWE; ISBN 978-989-758-386-5; ISSN 2184-3252, SciTePress, pages 470-476. DOI: 10.5220/0008559204700476

@conference{apmdwe19,
author={Antonio Martínez{-}Rojas. and Andrés Jiménez{-}Ramírez. and Jose G. Enríquez.},
title={Towards a Unified Model Representation of Machine Learning Knowledge},
booktitle={Proceedings of the 15th International Conference on Web Information Systems and Technologies - APMDWE},
year={2019},
pages={470-476},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008559204700476},
isbn={978-989-758-386-5},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Web Information Systems and Technologies - APMDWE
TI - Towards a Unified Model Representation of Machine Learning Knowledge
SN - 978-989-758-386-5
IS - 2184-3252
AU - Martínez-Rojas, A.
AU - Jiménez-Ramírez, A.
AU - Enríquez, J.
PY - 2019
SP - 470
EP - 476
DO - 10.5220/0008559204700476
PB - SciTePress