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Towards ML Models’ Recommendations

Towards ML Models’ Recommendations

Authors :
Lara Kallab
Elio Mansour
Richard Chbeir
Source :
Data Science and Engineering, Vol 9, Iss 4, Pp 409-430 (2024)
Publication Year :
2024
Publisher :
SpringerOpen, 2024.

Abstract

Abstract Artificial Intelligence encompasses a range of technologies that replicate human-like cognitive abilities through computer systems, enabling the execution of tasks associated with intelligent beings. A prominent way to achieve this is machine learning (ML), which optimizes system performance by employing learning algorithms to create models based on data and its inherent patterns. Today, a multitude of ML models exist having diverse characteristics, including the algorithm type, training dataset, and resultant performance. Such diversity complicates the selection of an appropriate model for a specific use case, answering user demands. This paper presents an approach for ML models retrieval based on the matching between user inputs and ML models criteria, all described in a semantic ML ontology named SML model (Semantic Machine Learning model), which facilitates the process of ML models selection. Our approach is based on similarities measures that we tested and experimented to score the ML models and retrieve the ones matching, at best, user inputs.

Details

Language :
English
ISSN :
23641185 and 23641541
Volume :
9
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Data Science and Engineering
Publication Type :
Academic Journal
Accession number :
edsdoj.4fe3d99abc0e48b6a7816c81212dbfc0
Document Type :
article
Full Text :
https://doi.org/10.1007/s41019-024-00262-x