SELM: Software Engineering of Machine Learning Models
Software Engineering
2021-03-23 v1 Artificial Intelligence
Abstract
One of the pillars of any machine learning model is its concepts. Using software engineering, we can engineer these concepts and then develop and expand them. In this article, we present a SELM framework for Software Engineering of machine Learning Models. We then evaluate this framework through a case study. Using the SELM framework, we can improve a machine learning process efficiency and provide more accuracy in learning with less processing hardware resources and a smaller training dataset. This issue highlights the importance of an interdisciplinary approach to machine learning. Therefore, in this article, we have provided interdisciplinary teams' proposals for machine learning.
Cite
@article{arxiv.2103.11249,
title = {SELM: Software Engineering of Machine Learning Models},
author = {Nafiseh Jafari and Mohammad Reza Besharati and Mohammad Izadi and Maryam Hourali},
journal= {arXiv preprint arXiv:2103.11249},
year = {2021}
}