Back to Search
Start Over
S3Mining: A model-driven engineering approach for supporting novice data miners in selecting suitable classifiers
- Source :
- Computer Standards and Interfaces, Volume 65, July 2019, Pages 143-158, UCrea Repositorio Abierto de la Universidad de Cantabria, Universidad de Cantabria (UC), RUA. Repositorio Institucional de la Universidad de Alicante, Universidad de Alicante (UA)
- Publication Year :
- 2019
- Publisher :
- Elsevier, 2019.
-
Abstract
- 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. This work has been partially funded by Spanish Government through the research projects TIN2017-86520-C3-3-R and TIN2016-78103-C2-2-R.
- Subjects :
- Meta learning (computer science)
Process (engineering)
Computer science
02 engineering and technology
Machine learning
computer.software_genre
Field (computer science)
Knowledge base
Novice data miners
Meta-learning
Estadística e Investigación Operativa
0202 electrical engineering, electronic engineering, information engineering
Selection (linguistics)
Data mining
computer.programming_language
Model-driven
business.industry
020206 networking & telecommunications
020207 software engineering
Workflow
Hardware and Architecture
Order (business)
Lenguajes y Sistemas Informáticos
Artificial intelligence
Model-driven architecture
Model-driven engineering
business
Law
computer
Software
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Computer Standards and Interfaces, Volume 65, July 2019, Pages 143-158, UCrea Repositorio Abierto de la Universidad de Cantabria, Universidad de Cantabria (UC), RUA. Repositorio Institucional de la Universidad de Alicante, Universidad de Alicante (UA)
- Accession number :
- edsair.doi.dedup.....907e53eccc91d4bab8f46657442b9b6d