TY - JOUR
T1 - Cost-conscious multiple kernel learning
AU - Gönen, Mehmet
AU - Alpaydin, Ethem
N1 - Funding Information:
This work was supported by the Turkish Academy of Sciences in the framework of the Young Scientist Award Program under EA-TÜBA-GEBİP/2001-1-1, the Boğaziçi University Scientific Research Project 07HA101 and the Turkish Scientific Technical Research Council (TÜBİTAK) under Grant EEEAG 107E222. The work of M. Gönen was supported by the Ph.D. scholarship (2211) from TÜBİTAK.
PY - 2010/7/1
Y1 - 2010/7/1
N2 - 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.
AB - 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.
KW - Kernel combination
KW - Multiple kernel learning
KW - Support vector machines
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U2 - 10.1016/j.patrec.2009.12.027
DO - 10.1016/j.patrec.2009.12.027
M3 - Article
AN - SCOPUS:77951135206
SN - 0167-8655
VL - 31
SP - 959
EP - 965
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
IS - 9
ER -