SML: Semantic Machine Learning Model Ontology
Résumé
Artificial Intelligence is a set of technologies that simulate human-like cognition, using computer software and systems, to perform tasks associated with intelligent beings. One method of doing so, is Machine Learning (ML), which enhances system efficiency based on learning algorithms that create models from data and its underlying patterns. Nowadays, many ML models are being generated with different characteristics (e.g., type of the algorithm used, data set used to train it, resulting model performance), thus making the selection of a suitable model for a given use case a complex task, especially for non-expert users (with no or limited knowledge in ML). In this paper, we propose SML, an ontology-based model for Semantic Machine Learning description. SML allows, mainly, to describe and store ML models' characteristics with their operational specifications, related data features, contextual usage, and evaluation metrics/scores to facilitate and improve ML model selection. The conducted experiments show promising results on both the efficiency and the performance levels.
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