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
Author: Bibal, A., 16 Nov 2020
Supervisor: FRENAY, B. (Supervisor), VANHOOF, W. (President), Cleve, A. (Jury), Dumas, B. (Jury), Lee, J. A. (External person) (Jury) & Galarraga, L. A. (External person) (Jury)
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",

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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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Student theses

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

Cite this