loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Matteo Cristani 1 ; Francesco Olvieri 2 ; Tewabe Chekole Workneh 1 ; Luca Pasetto 1 and Claudio Tomazzoli 3

Affiliations: 1 Department of Computer Science, University of Verona, Italy ; 2 School of Computer Science, Griffith University, Brisbane, Australia ; 3 CITERA, University of Rome, Italy

Keyword(s): Machine Learning, eXplainable AI, Approximation, Anytime Methods.

Abstract: We introduce a general model for explainable Artificial Intelligence that identifies an explanation of a Machine Learning method by classification rules. We define a notion of distance between two Machine Learning methods, and provide a method that computes a set of classification rules that, in turn, approximates another black box method to a given extent. We further build upon this method an anytime algorithm that returns the best approximation it can compute within a given interval of time. This anytime method returns the minimum and maximum difference in terms of approximation provided by the algorithm and uses it to determine whether the obtained approximation is acceptable. We then illustrate the results of a few experiments on three different datasets that show certain properties of the approximations that should be considered while modelling such systems. On top of this, we design a methodology for constructing approximations for ML, that we compare to the no-methods approach typically used in current studies on the explainable artificial intelligence topic. (More)

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 18.220.1.239

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:
Cristani, M.; Olvieri, F.; Workneh, T.; Pasetto, L. and Tomazzoli, C. (2022). Classification Rules Explain Machine Learning. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 897-904. DOI: 10.5220/0010927300003116

@conference{icaart22,
author={Matteo Cristani. and Francesco Olvieri. and Tewabe Chekole Workneh. and Luca Pasetto. and Claudio Tomazzoli.},
title={Classification Rules Explain Machine Learning},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2022},
pages={897-904},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010927300003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Classification Rules Explain Machine Learning
SN - 978-989-758-547-0
IS - 2184-433X
AU - Cristani, M.
AU - Olvieri, F.
AU - Workneh, T.
AU - Pasetto, L.
AU - Tomazzoli, C.
PY - 2022
SP - 897
EP - 904
DO - 10.5220/0010927300003116
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic