loading
Papers

Research.Publish.Connect.

Paper

Author: Guillaume Petiot

Affiliation: CERES, Catholic Institute of Toulouse, 31 rue de la Fonderie, Toulouse and France

ISBN: 978-989-758-350-6

Keyword(s): Formal Concept Analysis, Possibility Theory, Natural Language Processing, Neural Network, Data Mining.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Uncertainty in AI

Abstract: The Formal Concept Analysis (FCA) is a method of data analysis often used in data mining. This method proposes to build a collection of formal concepts from a set of objects and their properties. These formal concepts can be ordered to provide a concept lattice. Several researches have demonstrated a link between the possibility theory and the formal concept analysis. Thus, it is possible to take into account the uncertainties of the properties by using the possibility theory before propagating them during the computation of the formal concepts. We propose in this paper an experimentation of the uncertain formal concept analysis for the extraction of knowledge in a satisfaction questionnaire for a course of professionalization in bachelor. Some questions can be open questions where the answers provided by students are given freely. For this purpose, we perform a text mining processing in order to categorize and classify the answers. During this processing, uncertainties can appear. In this research, we will handle these uncertainties by using the uncertain formal concept analysis. Then, we will extract the uncertain formal concepts from the concept lattice by using queries and represent the reduced lattice concepts with a visualization tool. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.204.48.199

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Petiot, G. (2019). Uncertain Formal Concept Analysis for the Analyze of a Course Satisfaction Questionnaire.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 488-495. DOI: 10.5220/0007311504880495

@conference{icaart19,
author={Guillaume Petiot.},
title={Uncertain Formal Concept Analysis for the Analyze of a Course Satisfaction Questionnaire},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={488-495},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007311504880495},
isbn={978-989-758-350-6},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Uncertain Formal Concept Analysis for the Analyze of a Course Satisfaction Questionnaire
SN - 978-989-758-350-6
AU - Petiot, G.
PY - 2019
SP - 488
EP - 495
DO - 10.5220/0007311504880495

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.