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Authors: Solmaz Bagherpour ; Àngela Nebot and Francisco Mugica

Affiliation: Technical University of Catalonia (UPC), Spain

ISBN: 978-989-758-038-3

Keyword(s): Fuzzy Inductive Reasoning (FIR), Argument based Machine Learning (ABML), Hierarchical FIR, Zoo Benchmark.

Related Ontology Subjects/Areas/Topics: Decision Support Systems ; Formal Methods ; Neural Nets and Fuzzy Systems ; Simulation and Modeling

Abstract: Many of the inductive reasoning algorithms and techniques, including Fuzzy Inductive Reasoning (FIR), that learn from labelled data don’t provide the possibility of involving domain expert knowledge to induce rules. In those cases that learning fails, this capability can guide the learning mechanism towards a hypothesis that seems more promising to a domain expert. One of the main reasons for omitting such involvement is the difficulty of knowledge acquisition from experts and, also, the difficulty of combining it with induced hypothesis. One of the successful solutions to such a problem is an alternative approach in machine learning called Argument Based Machine Learning (ABML) which involves experts in providing specific explanations in the form of arguments to only specific cases that fail, rather than general knowledge on all cases. Inspired by this study, the idea of Hierarchical Fuzzy Inductive Reasoning (HFIR) is proposed in this paper as the first step towards design and devel opment of an Argument Based Fuzzy Inductive Reasoning method capable of providing domain expert involvement in its induction process. Moreover, HFIR is able to obtain better classifications results than classical FIR methodology. In this work, the concept of Hierarchical Fuzzy Inductive Reasoning is introduced and explored by means of the Zoo UCI benchmark. (More)

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Paper citation in several formats:
Bagherpour, S.; Nebot, À. and Mugica, F. (2014). Hierarchical Fuzzy Inductive Reasoning Classifier.In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-758-038-3, pages 434-442. DOI: 10.5220/0005041604340442

@conference{simultech14,
author={Solmaz Bagherpour. and Àngela Nebot. and Francisco Mugica.},
title={Hierarchical Fuzzy Inductive Reasoning Classifier},
booktitle={Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2014},
pages={434-442},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005041604340442},
isbn={978-989-758-038-3},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - Hierarchical Fuzzy Inductive Reasoning Classifier
SN - 978-989-758-038-3
AU - Bagherpour, S.
AU - Nebot, À.
AU - Mugica, F.
PY - 2014
SP - 434
EP - 442
DO - 10.5220/0005041604340442

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