Use of a i*extension for Machine Learning: a real case study

Please use this identifier to cite or link to this item:
Información del item - Informació de l'item - Item information
Title: Use of a i*extension for Machine Learning: a real case study
Authors: Barrera, Jose Manuel | Reina Reina, Alejandro | García-Ponsoda, Sandra | Trujillo, Juan
Research Group/s: Lucentia
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: Machine learning | iStar | Requirements engineering | Conceptual modelling | Methodology
Issue Date: 6-Oct-2022
Publisher: CEUR
Citation: iStar’22: The 15th International i* Workshop, October 17th, 2022, Hyderabad, India. CEUR Workshop Proceedings, Vol-3231, 14-20
Abstract: Capturing requirements in machine learning projects is a challenging task. It requires domain knowledge as well as experience in the machine learning field. The i* framework is a popular high abstraction-layer requirements capturing tool. However, the use of i* directly in the machine learning field (ML) is unfeasible due to it cannot capture all the restrictions and relationships of ML elements. In previous works we have extended i* to better capture machine learning requirements. In this paper, we apply the i* for machine learning extension to a real machine learning case study, in the context of a project focused on the diagnosis and treatment of Attention-Deficit/Hyperactivity Disorder (ADHD). The results show that the use of the i* for machine learning extension provides insights about the correct path to follow, aiding in the definition and selection of machine learning solutions that better fulfill the project requirements. Moreover, it facilitates faster development of the machine learning solution in a more structured way, avoiding errors and making the application of i* an effective tool for managing machine learning requirements.
ISSN: 1613-0073
Language: eng
Type: info:eu-repo/semantics/conferenceObject
Rights: © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Peer Review: si
Publisher version:
Appears in Collections:INV - LUCENTIA - Comunicaciones a Congresos, Conferencias, etc.

Files in This Item:
Files in This Item:
File Description SizeFormat 
ThumbnailBarrera_etal_iStar22.pdf467,88 kBAdobe PDFOpen Preview

This item is licensed under a Creative Commons License Creative Commons