Towards a Unified Model Representation of Machine Learning Knowledge

Antonio Martínez-Rojas, Andrés Jiménez-Ramírez, Jose G. Enríquez

2019

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.

Paper Citation

in Harvard Style

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 - Volume 1: APMDWE, ISBN 978-989-758-386-5, pages 470-476. DOI: 10.5220/0008559204700476

in Bibtex Style

@conference{apmdwe19,
author={Antonio Martínez-Rojas and Andrés Jiménez-Ramírez and Jose Enríquez},
title={Towards a Unified Model Representation of Machine Learning Knowledge},
booktitle={Proceedings of the 15th International Conference on Web Information Systems and Technologies - Volume 1: APMDWE,},
year={2019},
pages={470-476},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008559204700476},
isbn={978-989-758-386-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Web Information Systems and Technologies - Volume 1: APMDWE,
TI - Towards a Unified Model Representation of Machine Learning Knowledge
SN - 978-989-758-386-5
AU - Martínez-Rojas A.
AU - Jiménez-Ramírez A.
AU - Enríquez J.
PY - 2019
SP - 470
EP - 476
DO - 10.5220/0008559204700476