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Anne Geoghegan

dblp:198/6532 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Trustworthy machine learning · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Environmental and earth informatics · 50% Medical and health informatics · 50%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning › interpretability
example-based explanation		 0.412020
Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract) · IJCAI 2020
Machine learning › Trustworthy machine learning
interpretability		 0.412020
Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract) · IJCAI 2020
Medical and health informatics › clinical decision support
decision support system		 0.112020
Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract) · IJCAI 2020
Environmental and earth informatics › agriculture
smart agriculture		 0.112020
Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract) · IJCAI 2020

Methods — techniques the papers use, named apart from their topics

case-based reasoning · 0.9bayesian methods · 0.9
YearPublicationVenuePosition
2020 Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract)
abstract
Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues. This paper reports a case-based reasoning (CBR) system, PBI-CBR, that predicts grass growth for dairy farmers, that combines predictive accuracy and explanations to improve user adoption. PBI-CBR’s key novelty is its use of Bayesian methods for case-base maintenance in a regression domain. Experiments report the tradeoff between predictive accuracy and explanatory capability for different variants of PBI-CBR, and how updating Bayesian priors each year improves performance.
Eoin M. Kenny, Elodie Ruelle, Anne Geoghegan, Laurence Shalloo, Micheál O'Leary, Michael O'Donovan, Mohammed Temraz, Mark T. Keane
 IJCAI3
2019 Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI)
Eoin M. Kenny, Elodie Ruelle, Anne Geoghegan, Laurence Shalloo, Micheál O'Leary, Michael O'Donovan, Mark T. Keane
 ICCBR3