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

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

35 Téléchargements (Pure)

Résumé

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.

langue originale	Anglais
titre	NIPS Workshop on Interpretable Machine Learning in Complex Systems
Lieu de publication	Barcelona
Etat de la publication	Publié - 2016
Evénement	Thirtieth Conference on Neural Information Processing Systems - Sagrada Familia, Barcelonne, Espagne Durée: 5 déc. 2016 → 10 déc. 2016

Colloque

Colloque	Thirtieth Conference on Neural Information Processing Systems
Titre abrégé	NIPS 2016 (NeurIPS)
Pays/Territoire	Espagne
La ville	Barcelonne
période	5/12/16 → 10/12/16

Accès au document

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
Auteur: Bibal, A., 16 nov. 2020
Superviseur: FRENAY, B. (Promoteur), VANHOOF, W. (Président), Cleve, A. (Jury), Dumas, B. (Jury), Lee, J. A. (Personne externe) (Jury) & Galarraga, L. A. (Personne externe) (Jury)
Student thesis: Doc types › Docteur en Sciences

Fichier

Contient cette citation

@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",

}

Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment. / Bibal, Adrien ; Frénay, Benoît.
NIPS Workshop on Interpretable Machine Learning in Complex Systems. Barcelona, 2016.

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

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

Résumé

Colloque

Accès au document

Empreinte digitale

Thèses de l'étudiant

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

Contient cette citation