Advanced search
2 files | 2.05 MB

A sequential sampling strategy for adaptive classification of computationally expensive data

Prashant Singh (UGent) , Joachim van der Herten (UGent) , Dirk Deschrijver (UGent) , Ivo Couckuyt (UGent) and Tom Dhaene (UGent)
Author
Organization
Abstract
Many real-world problems in engineering can be represented and solved as a data-driven classification problem, where the goal is to build a classifier that maps a given set of input parameters onto a corresponding class or label. In some cases, the collection of data samples can be computationally expensive. It is therefore crucial to solve the problem using as little data as possible. To this end, a novel sequential sampling algorithm is proposed that begins with a very small training set and supplements it in each iteration by a small batch of additional (expensive) data points. The outcome is a representative set of data samples that focuses the sampling on those locations in the input space where the class labels are changing more rapidly, while making sure that no class regions are missed.
Keywords
IBCN

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 1.53 MB
  • 7014 i.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 513.62 KB

Citation

Please use this url to cite or link to this publication:

Chicago
Singh, Prashant, Joachim van der Herten, Dirk Deschrijver, Ivo Couckuyt, and Tom Dhaene. 2017. “A Sequential Sampling Strategy for Adaptive Classification of Computationally Expensive Data.” Structural and Multidisciplinary Optimization 55 (4): 1425–1438.
APA
Singh, Prashant, van der Herten, J., Deschrijver, D., Couckuyt, I., & Dhaene, T. (2017). A sequential sampling strategy for adaptive classification of computationally expensive data. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 55(4), 1425–1438.
Vancouver
1.
Singh P, van der Herten J, Deschrijver D, Couckuyt I, Dhaene T. A sequential sampling strategy for adaptive classification of computationally expensive data. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION. 2017;55(4):1425–38.
MLA
Singh, Prashant et al. “A Sequential Sampling Strategy for Adaptive Classification of Computationally Expensive Data.” STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 55.4 (2017): 1425–1438. Print.
@article{8541163,
  abstract     = {Many real-world problems in engineering can be represented and solved as a data-driven classification problem, where the goal is to build a classifier that maps a given set of input parameters onto a corresponding class or label. In some cases, the collection of data samples can be computationally expensive. It is therefore crucial to solve the problem using as little data as possible. To this end, a novel sequential sampling algorithm is proposed that begins with a very small training set and supplements it in each iteration by a small batch of additional (expensive) data points. The outcome is a representative set of data samples that focuses the sampling on those locations in the input space where the class labels are changing more rapidly, while making sure that no class regions are missed.},
  author       = {Singh, Prashant and van der Herten, Joachim and Deschrijver, Dirk and Couckuyt, Ivo and Dhaene, Tom},
  issn         = {1615-147X},
  journal      = {STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION},
  keywords     = {IBCN},
  language     = {eng},
  number       = {4},
  pages        = {1425--1438},
  title        = {A sequential sampling strategy for adaptive classification of computationally expensive data},
  url          = {http://dx.doi.org/10.1007/s00158-016-1584-1},
  volume       = {55},
  year         = {2017},
}

Altmetric
View in Altmetric
Web of Science
Times cited: