Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment

Adrien Bibal; Benoît Frénay

Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment

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Abstract

In order to be useful, visualizations need to be interpretable. This paper uses a user-based approach to combine and assess quality measures in order to better model user preferences. Results show that cluster separability measures are outperformed by a neighborhood conservation measure, even though the former are usually considered as intuitively representative of user motives. Moreover, combining measures, as opposed to using a single measure, further improves prediction performances.

Original language	English
Title of host publication	NIPS Workshop on Interpretable Machine Learning in Complex Systems
Place of Publication	Barcelona
Publication status	Published - 2016
Event	Thirtieth Conference on Neural Information Processing Systems - Sagrada Familia, Barcelonne, Spain Duration: 5 Dec 2016 → 10 Dec 2016

Symposium

Symposium	Thirtieth Conference on Neural Information Processing Systems
Abbreviated title	NIPS 2016 (NeurIPS)
Country/Territory	Spain
City	Barcelonne
Period	5/12/16 → 10/12/16

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2016-Bibal-Learning Interpretability for Visualizations using Adapted Cox Models through a User ExperimentAccepted author manuscript, 281 KB

Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction
Bibal, A. (Author)FRENAY, B. (Supervisor), VANHOOF, W. (President), Cleve, A. (Jury), Dumas, B. (Jury), Lee, J. A. (Jury) & Galarraga, L. (Jury), 16 Nov 2020
Student thesis: Doc types › Doctor of Sciences

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@inproceedings{30b04314ee184247bbb75eb4dc56551a,

title = "Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment",

abstract = "In order to be useful, visualizations need to be interpretable. This paper uses a user-based approach to combine and assess quality measures in order to better model user preferences. Results show that cluster separability measures are outperformed by a neighborhood conservation measure, even though the former are usually considered as intuitively representative of user motives. Moreover, combining measures, as opposed to using a single measure, further improves prediction performances.",

author = "Adrien Bibal and Beno{\^i}t Fr{\'e}nay",

year = "2016",

language = "English",

booktitle = "NIPS Workshop on Interpretable Machine Learning in Complex Systems",

note = "Thirtieth Conference on Neural Information Processing Systems, NIPS 2016 (NeurIPS) ; Conference date: 05-12-2016 Through 10-12-2016",

}

TY - GEN

T1 - Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment

AU - Bibal, Adrien

AU - Frénay, Benoît

PY - 2016

Y1 - 2016

N2 - In order to be useful, visualizations need to be interpretable. This paper uses a user-based approach to combine and assess quality measures in order to better model user preferences. Results show that cluster separability measures are outperformed by a neighborhood conservation measure, even though the former are usually considered as intuitively representative of user motives. Moreover, combining measures, as opposed to using a single measure, further improves prediction performances.

AB - In order to be useful, visualizations need to be interpretable. This paper uses a user-based approach to combine and assess quality measures in order to better model user preferences. Results show that cluster separability measures are outperformed by a neighborhood conservation measure, even though the former are usually considered as intuitively representative of user motives. Moreover, combining measures, as opposed to using a single measure, further improves prediction performances.

M3 - Conference contribution

BT - NIPS Workshop on Interpretable Machine Learning in Complex Systems

CY - Barcelona

T2 - Thirtieth Conference on Neural Information Processing Systems

Y2 - 5 December 2016 through 10 December 2016

ER -

Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment

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Symposium

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Student theses

Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction

Cite this