Hierarchical Fuzzy Inductive Reasoning Classifier

Solmaz Bagherpour, Àngela Nebot, Francisco Mugica

2014

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 development 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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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