Cost-conscious multiple kernel learning

Mehmet Gönen, Ethem Alpaydin

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Recently, it has been proposed to combine multiple kernels using a weighted linear sum. In certain applications, different kernels may be using different input representations and these methods do not consider neither the cost of acquiring them nor the cost of evaluating the kernels. We generalize the framework of Multiple Kernel Learning (Mkl) for this cost-conscious methodology. On 12 benchmark data sets from the UCI repository, we compare Mkl and its cost-conscious variants in terms of accuracy, support vector count, and total cost. Cost-conscious Mkl achieves statistically similar accuracy results by using fewer support vectors/kernels by best trading off accuracy brought by each representation/kernel with the concomitant cost. We also test our approach on two popular bioinformatics data sets from MIPS comprehensive yeast genome database (CYGD) and see that integrating the cost factor into kernel combination allows us to obtain cheaper kernel combinations by using fewer active kernels and/or support vectors.

Original languageEnglish (US)
Pages (from-to)959-965
Number of pages7
JournalPattern Recognition Letters
Issue number9
StatePublished - Jul 1 2010
Externally publishedYes


  • Kernel combination
  • Multiple kernel learning
  • Support vector machines

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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