More Interpretable Decision Trees

Gilmore, Eugene; Estivill‐Castro, Vladimir; Hexel, René

doi:10.1007/978-3-030-86271-8_24

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…However, decisions being based on over 100 variables are hardly interpretable and understandable [56]. Our method is an improvement over the NC algorithm [57]. Moreover, our prototype uses ML metrics to recommend attributes (and their order) in a visualisation.…”

Section: Dataset Visualisations For Involving Experts In Classifier C...mentioning

confidence: 99%

Constructing Explainable Classifiers from the Start—Enabling Human-in-the Loop Machine Learning

2022

Self Cite

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Interactive machine learning (IML) enables the incorporation of human expertise because the human participates in the construction of the learned model. Moreover, with human-in-the-loop machine learning (HITL-ML), the human experts drive the learning, and they can steer the learning objective not only for accuracy but perhaps for characterisation and discrimination rules, where separating one class from others is the primary objective. Moreover, this interaction enables humans to explore and gain insights into the dataset as well as validate the learned models. Validation requires transparency and interpretable classifiers. The huge relevance of understandable classification has been recently emphasised for many applications under the banner of explainable artificial intelligence (XAI). We use parallel coordinates to deploy an IML system that enables the visualisation of decision tree classifiers but also the generation of interpretable splits beyond parallel axis splits. Moreover, we show that characterisation and discrimination rules are also well communicated using parallel coordinates. In particular, we report results from the largest usability study of a IML system, confirming the merits of our approach.

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Section: Dataset Visualisations For Involving Experts In Classifier C...mentioning

confidence: 99%

Constructing Explainable Classifiers from the Start—Enabling Human-in-the Loop Machine Learning

2022

Self Cite

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“…Decision trees [3,4,8,29,32,38] and decision rule systems [6,7,11,12,31,32,33,34] are widely used as a means for knowledge representation, as classifiers that predict decisions for new objects, and as algorithms for solving various problems of fault diagnosis, combinatorial optimization, etc. Decision trees and rules are among the most interpretable models for classifying and representing knowledge [10,13,21,39].…”

Section: Introductionmentioning

confidence: 99%

Bounds on Depth of Decision Trees Derived from Decision Rule Systems

Durdymyradov¹,

Moshkov²

2023

Preprint

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Systems of decision rules and decision trees are widely used as a means for knowledge representation, as classifiers, and as algorithms. They are among the most interpretable models for classifying and representing knowledge. The study of relationships between these two models is an important task of computer science. It is easy to transform a decision tree into a decision rule system. The inverse transformation is a more difficult task. In this paper, we study unimprovable upper and lower bounds on the minimum depth of decision trees derived from decision rule systems depending on the various parameters of these systems.

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Simulating Functioning of Decision Trees for Tasks on Decision Rule Systems

Durdymyradov,

Moshkov

2024

Rough Sets

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More Interpretable Decision Trees

Cited by 6 publications

References 24 publications

Constructing Explainable Classifiers from the Start—Enabling Human-in-the Loop Machine Learning

Constructing Explainable Classifiers from the Start—Enabling Human-in-the Loop Machine Learning

Bounds on Depth of Decision Trees Derived from Decision Rule Systems

Simulating Functioning of Decision Trees for Tasks on Decision Rule Systems

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