Complicacy-guided parameter space sampling for knowledge discovery with limited simulation budgets

Xilun Chen, Logan Mathesen, Giulia Pedrielli, K. Selçuk Candan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Knowledge discovery and decision making through data-and model-driven computer simulation ensembles are increasingly critical in many application domains. However, these simulation ensembles are expensive to obtain. Consequently, given a relatively small simulation budget, one needs to identify a sparse ensemble that includes the most informative simulations to help the effective exploration of the space of input parameters. In this paper, we propose a complicacy-guided parameter space sampling (CPSS) for knowledge discovery with limited simulation budgets, which relies on a novel complicacy-driven guidance mechanism to rank candidate models and a novel rank-stability based parameter space partitioning strategy to identify simulation instances to execute. The advantage of the proposed approach is that, unlike purely fit-based approaches, it avoids extensive simulations in difficult-to-fit regions of the parameter space, if the region can be explained with a much simpler model, requiring fewer simulation samples, even if with a slightly lower fit.

Original languageEnglish (US)
Title of host publicationProceedings - 10th IEEE International Conference on Big Knowledge, ICBK 2019
EditorsYunjun Gao, Ralf Moller, Xindong Wu, Ramamohanarao Kotagiri
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages50-57
Number of pages8
ISBN (Electronic)9781728146065
DOIs
StatePublished - Nov 2019
Event10th IEEE International Conference on Big Knowledge, ICBK 2019 - Beijing, China
Duration: Nov 10 2019Nov 11 2019

Publication series

NameProceedings - 10th IEEE International Conference on Big Knowledge, ICBK 2019

Conference

Conference10th IEEE International Conference on Big Knowledge, ICBK 2019
CountryChina
CityBeijing
Period11/10/1911/11/19

Keywords

  • Complicacy guided sampling
  • Knowledge discovery
  • Parameter space sampling
  • Simulation

ASJC Scopus subject areas

  • Information Systems
  • Decision Sciences (miscellaneous)
  • Information Systems and Management
  • Management Science and Operations Research
  • Safety, Risk, Reliability and Quality

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