Open Science Research Excellence
@article{(International Science Index):,
  title    = {An ensemble of Weighted Support Vector Machines for Ordinal Regression},
  author    = {Willem Waegeman and  Luc Boullart},
  country   = {},
  abstract  = {Instead of traditional (nominal) classification we investigate
the subject of ordinal classification or ranking. An enhanced
method based on an ensemble of Support Vector Machines (SVM-s)
is proposed. Each binary classifier is trained with specific weights
for each object in the training data set. Experiments on benchmark
datasets and synthetic data indicate that the performance of our
approach is comparable to state of the art kernel methods for
ordinal regression. The ensemble method, which is straightforward
to implement, provides a very good sensitivity-specificity trade-off
for the highest and lowest rank.},
    journal   = {International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering},  volume    = {1},
  number    = {12},
  year      = {2007},
  pages     = {599 - 603},
  ee        = {},
  url       = {},
  bibsource = {},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 12, 2007},