White box radial basis function classifiers with component selection for clinical prediction models

  title={White box radial basis function classifiers with component selection for clinical prediction models},
  author={Vanya Van Belle and Paulo J. G. Lisboa},
  journal={Artificial intelligence in medicine},
  volume={60 1},
OBJECTIVE To propose a new flexible and sparse classifier that results in interpretable decision support systems. METHODS Support vector machines (SVMs) for classification are very powerful methods to obtain classifiers for complex problems. Although the performance of these methods is consistently high and non-linearities and interactions between variables can be handled efficiently when using non-linear kernels such as the radial basis function (RBF) kernel, their use in domains where… CONTINUE READING
Highly Cited
This paper has 17 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-9 of 9 extracted citations


Publications referenced by this paper.
Showing 1-10 of 44 references

Decision treebased feature ranking using Manhattan hierarchical cluster criterion

  • YM Yacob, HA MatSakim, NA MatIsa
  • International Journal of Engineering and Physical…
  • 2012

Application of core vector machines for on - line voltage security assessment using a decisiontreebased feature selection algorithm

  • M Mohammadi, GB Gharehpetian
  • IET Generation Transmission Distribution
  • 2009

Feature ranking by weighting and ISE criterion of nonparametric density estimation

  • X Wang, S Wang
  • Journal of Applied Sciences
  • 2009
1 Excerpt

Feature selection using FCBF in type II diabetes databases

  • S Balakrishnan, R Narayanaswamy
  • International Journal of the Computer , the…
  • 2009

Similar Papers

Loading similar papers…