• 0 Citations
20172019
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Simon van der Zon is a second year PhD researcher at the Mathematics and Computer Science department at Eindhoven University of Technology. His doctoral research investigates various aspects of Interactive Machine Learning. He considers the interactive machine learning cycle to consist of two parts: on the one hand interpretation of machine learning models, and on the other, imposing changes on the models using domain knowledge, not captured in the data. Interpretation and transparency is achieved through generation of explanations for decisions, where the concept of “context” plays an important role. Once a basic level on understanding of the model is obtained, one might want to impose changes on the model; to alter the model’s decision making he is in particular interested in the case where the model is in charge of (actively) requesting knowledge from the user in order to learn a better (i.e. more accurate, fair, unbiased, ethical) model. His research is employed at Rabobank, where topics such as transparency and ethics are of a major concern.

Simon received his master degree at Eindhoven University of Technology with his master thesis titled “Predictive Performance and Discrimination in Unbalanced Classification”, where he investigated the relationships between removal of discrimination in unbalanced data and classifier performance.

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Research Output 2017 2019

  • 2 Conference contribution

ICIE 1.0: a novel tool for interactive contextual interaction explanations

van der Zon, S. B., Duivesteijn, W., van Ipenburg, W., Veldsink, J. & Pechenizkiy, M., 1 Jan 2019, ECML PKDD 2018 Workshops - MIDAS 2018 and PAP 2018, Proceedings. Monreale, A. & Alzate, C. (eds.). Cham: Springer, p. 81-94 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11054 LNAI)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Interaction
Banking
Insurance
Privacy
Transactions

BoostEMM : Transparent boosting using exceptional model mining

van der Zon, S. B., Zeev Ben Mordehay, O., Vrijdag, T. S., van Ipenburg, W., Veldsink, J., Duivesteijn, W. & Pechenizkiy, M., 2017, Proceedings of the Second Workshop on MIning DAta for financial applicationS (MIDAS 2017), 18 September 2017, Skopje, Macedonia . Bordino, I., Caldarelli, G., Fumarola, F., Gullo, F. & Squartini, T. (eds.). p. 5-16 12 p. (CEUR Workshop Proceedings; vol. 1941) (CEUR Workshop Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Open Access
File
Adaptive boosting
Transparency

Student theses

Application-grounded evaluation of predictive model explanation methods

Author: Lin, C., 24 Sep 2018

Supervisor: Pechenizkiy, M. (Supervisor 1), van der Zon, S. (Supervisor 2), van Ipenburg, W. (External person) (External coach), Veldsink, J. W. (External person) (External coach) & Papapetrou, O. (Supervisor 2)

Student thesis: Master

File

Concept drift investigation

Author: Zhang, M., 26 Mar 2018

Supervisor: Pechenizkiy, M. (Supervisor 1), van Ipenburg, W. (External person) (External coach) & van der Zon, S. (Supervisor 2)

Student thesis: Master

File

Framework for multi-view predictive visual analytics for a customer due diligence report

Author: González Huesca, J., 31 Aug 2018

Supervisor: Pechenizkiy, M. (Supervisor 1), van der Zon, S. (Supervisor 2), van Ipenburg, W. (External person) (External coach) & de Leoni, M. (Supervisor 2)

Student thesis: Master

File

Predictive performance and discrimination in unbalanced classification

Author: van der Zon, S., 31 Oct 2016

Supervisor: Pechenizkiy, M. (Supervisor 1), Zannone, N. (Supervisor 2) & Serebrenik, A. (Supervisor 2)

Student thesis: Master

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