short-paper

Transparent Tree Ensembles

Authors:

Alexander Moore,

Vanessa Murdock,

Yaxiong Cai,

Kristine JonesAuthors Info & Claims

SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

Pages 1241 - 1244

https://doi.org/10.1145/3209978.3210151

Published: 27 June 2018 Publication History

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Abstract

Every day more technologies and services are backed by complex machine-learned models, consuming large amounts of data to provide a myriad of useful services. While users are willing to provide personal data to enable these services, their trust in and engagement with the systems could be improved by providing insight into how the machine learned decisions were made. Complex ML systems are highly effective but many of them are black boxes and give no insight into how they make the choices they make. Moreover, those that do often do so at the model-level rather than the instance-level. In this work we present a method for deriving explanations for instance-level decisions in tree ensembles. As this family of models accounts for a large portion of industrial machine learning, this work opens up the possibility for transparent models at scale.

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Cited By

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Dedja KNakano FPliakos KVens C(2023)BELLATREX: Building Explanations Through a LocaLly AccuraTe Rule EXtractorIEEE Access10.1109/ACCESS.2023.326886611(41348-41367)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3268866
Mylonas NMollas IBassiliades NTsoumakas G(2023)Local Multi-label Explanations for Random ForestMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-23618-1_25(369-384)Online publication date: 31-Jan-2023
https://doi.org/10.1007/978-3-031-23618-1_25
Estivill-Castro VGilmore EHexel R(2022)Constructing Explainable Classifiers from the Start—Enabling Human-in-the Loop Machine LearningInformation10.3390/info1310046413:10(464)Online publication date: 29-Sep-2022
https://doi.org/10.3390/info13100464
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Index Terms

Transparent Tree Ensembles
1. Computing methodologies
  1. Machine learning

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Information

Published In

SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

June 2018

1509 pages

ISBN:9781450356572

DOI:10.1145/3209978

General Chairs:
Kevyn Collins-Thompson
University of Michigan, United States
,
Qiaozhu Mei
University of Michigan, United States
,
Program Chairs:
Brian Davison
Lehigh University, United States
,
Yiqun Liu
Tsinghua University, China
,
Emine Yilmaz
University College London, United Kingdom

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 June 2018

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SIGIR '18

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SIGIR

SIGIR '18: The 41st International ACM SIGIR conference on research and development in Information Retrieval

July 8 - 12, 2018

MI, Ann Arbor, USA

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SIGIR '18 Paper Acceptance Rate 86 of 409 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Citations

Cited By

View all

Dedja KNakano FPliakos KVens C(2023)BELLATREX: Building Explanations Through a LocaLly AccuraTe Rule EXtractorIEEE Access10.1109/ACCESS.2023.326886611(41348-41367)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3268866
Mylonas NMollas IBassiliades NTsoumakas G(2023)Local Multi-label Explanations for Random ForestMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-23618-1_25(369-384)Online publication date: 31-Jan-2023
https://doi.org/10.1007/978-3-031-23618-1_25
Estivill-Castro VGilmore EHexel R(2022)Constructing Explainable Classifiers from the Start—Enabling Human-in-the Loop Machine LearningInformation10.3390/info1310046413:10(464)Online publication date: 29-Sep-2022
https://doi.org/10.3390/info13100464
Mollas IBassiliades NTsoumakas G(2022)Altruist: Argumentative Explanations through Local Interpretations of Predictive ModelsProceedings of the 12th Hellenic Conference on Artificial Intelligence10.1145/3549737.3549762(1-10)Online publication date: 7-Sep-2022
https://dl.acm.org/doi/10.1145/3549737.3549762
Murphy AMurphy GDias DAmaral JNaredo ERyan C(2022)Human in the Loop Fuzzy Pattern Tree EvolutionSN Computer Science10.1007/s42979-022-01044-w3:2Online publication date: 14-Feb-2022
https://doi.org/10.1007/s42979-022-01044-w
Mollas IBassiliades NTsoumakas G(2022)Conclusive local interpretation rules for random forestsData Mining and Knowledge Discovery10.1007/s10618-022-00839-y36:4(1521-1574)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s10618-022-00839-y
Estivill-Castro VGilmore EHexel R(2022)Interpretable Decisions Trees via Human-in-the-Loop-LearningData Mining10.1007/978-981-19-8746-5_9(115-130)Online publication date: 5-Dec-2022
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Estivill-Castro VGilmore EHexel R(2020)Human-In-The-Loop Construction of Decision Tree Classifiers with Parallel Coordinates2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC42975.2020.9283240(3852-3859)Online publication date: 11-Oct-2020
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Estivill-Castro VGilmore EHexel R(2020)Constructing Interpretable Decision Trees Using Parallel CoordinatesArtificial Intelligence and Soft Computing10.1007/978-3-030-61534-5_14(152-164)Online publication date: 7-Oct-2020
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Thomas PBillerbeck BCraswell NWhite R(2019)Investigating Searchers’ Mental Models to Inform Search ExplanationsACM Transactions on Information Systems10.1145/337139038:1(1-25)Online publication date: 20-Dec-2019
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