S3Mining: A model-driven engineering approach for supporting novice data miners in selecting suitable classifiers
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Título: | S3Mining: A model-driven engineering approach for supporting novice data miners in selecting suitable classifiers |
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Autor/es: | Espinosa, Roberto | García-Saiz, Diego | Zorrilla Pantaleón, Marta | Zubcoff, Jose | Mazón, Jose-Norberto |
Grupo/s de investigación o GITE: | Web and Knowledge (WaKe) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencias del Mar y Biología Aplicada | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Palabras clave: | Data mining | Knowledge base | Model-driven engineering | Meta-learning | Novice data miners | Model-driven |
Área/s de conocimiento: | Estadística e Investigación Operativa | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | jul-2019 |
Editor: | Elsevier |
Cita bibliográfica: | Computer Standards & Interfaces. 2019, 65: 143-158. doi:10.1016/j.csi.2019.03.004 |
Resumen: | Data mining has proven to be very useful in order to extract information from data in many different contexts. However, due to the complexity of data mining techniques, it is required the know-how of an expert in this field to select and use them. Actually, adequately applying data mining is out of the reach of novice users which have expertise in their area of work, but lack skills to employ these techniques. In this paper, we use both model-driven engineering and scientific workflow standards and tools in order to develop named S3Mining framework, which supports novice users in the process of selecting the data mining classification algorithm that better fits with their data and goal. To this aim, this selection process uses the past experiences of expert data miners with the application of classification techniques over their own datasets. The contributions of our S3Mining framework are as follows: (i) an approach to create a knowledge base which stores the past experiences of experts users, (ii) a process that provides the expert users with utilities for the construction of classifiers’ recommenders based on the existing knowledge base, (iii) a system that allows novice data miners to use these recommenders for discovering the classifiers that better fit for solving their problem at hand, and (iv) a public implementation of the framework’s workflows. Finally, an experimental evaluation has been conducted to shown the feasibility of our framework. |
Patrocinador/es: | This work has been partially funded by Spanish Government through the research projects TIN2017-86520-C3-3-R and TIN2016-78103-C2-2-R. |
URI: | http://hdl.handle.net/10045/91707 |
ISSN: | 0920-5489 (Print) | 1872-7018 (Online) |
DOI: | 10.1016/j.csi.2019.03.004 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2019 Elsevier B.V. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.csi.2019.03.004 |
Aparece en las colecciones: | INV - WaKe - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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2019_Espinosa_etal_ComputerStandards&Interfaces_final.pdf | Versión final (acceso restringido) | 6,58 MB | Adobe PDF | Abrir Solicitar una copia |
2019_Espinosa_etal_ComputerStandards&Interfaces_preprint.pdf | Preprint (acceso abierto) | 2,23 MB | Adobe PDF | Abrir Vista previa |
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