Optimizing Decision Trees for Enhanced Human Comprehension

Ruth Cohen Arbiv, Laurence Lovat, Avi Rosenfeld, David Sarne

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper studies a novel approach for training people to perform complex classification tasks using decision trees. The main objective of this study is to identify the most effective subset of rules for instructing users on how to excel in classification tasks themselves. The paper addresses the challenge of striking a balance between maximizing knowledge by incorporating numerous rules and the need to limit rules to prevent cognitive overload. To investigate this matter, a series of experiments were conducted, training users using decision trees to identify cases where cancer is suspected, and further testing is required. Notably, the study revealed a correlation between the decision tree characteristics and users’ comprehension levels. Building on these experimental outcomes, a machine learning model was developed to predict users’ comprehension levels based on different decision trees, thereby facilitating the selection of the most appropriate tree. To further assess the machine learning model’s performance, additional experiments were carried out using an alternative dataset focused on Crohn’s disease. The results demonstrated a significant enhancement in user understanding and classification performance. These findings emphasize the potential to improve human understanding and decision rule explainability by effectively modeling users’ comprehension.

Original languageEnglish
Title of host publicationArtificial Intelligence. ECAI 2023 International Workshops - XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, 2023, Proceedings
EditorsSławomir Nowaczyk, Przemysław Biecek, Neo Christopher Chung, Mauro Vallati, Paweł Skruch, Joanna Jaworek-Korjakowska, Simon Parkinson, Alexandros Nikitas, Martin Atzmüller, Tomáš Kliegr, Ute Schmid, Szymon Bobek, Nada Lavrac, Marieke Peeters, Roland van Dierendonck, Saskia Robben, Eunika Mercier-Laurent, Gülgün Kayakutlu, Mieczyslaw Lech Owoc, Karl Mason, Abdul Wahid, Pierangela Bruno, Francesco Calimeri, Francesco Cauteruccio, Giorgio Terracina, Diedrich Wolter, Jochen L. Leidner, Michael Kohlhase, Vania Dimitrova
PublisherSpringer Science and Business Media Deutschland GmbH
Pages366-381
Number of pages16
ISBN (Print)9783031503955
DOIs
StatePublished - 2024
EventInternational Workshops of the 26th European Conference on Artificial Intelligence, ECAI 2023 - Kraków, Poland
Duration: 30 Sep 20234 Oct 2023

Publication series

NameCommunications in Computer and Information Science
Volume1947
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

ConferenceInternational Workshops of the 26th European Conference on Artificial Intelligence, ECAI 2023
Country/TerritoryPoland
CityKraków
Period30/09/234/10/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

  • Adaptive User Modeling
  • Explainable Artificial Intelligence
  • Medical Diagnoses

Fingerprint

Dive into the research topics of 'Optimizing Decision Trees for Enhanced Human Comprehension'. Together they form a unique fingerprint.

Cite this