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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/8477

Title: Simple but effective methods for combining kernels in computational biology
Authors: Tanabe, Hiroaki.
Ho, Tu Bao
Nguyen, Canh Hao
Kawasaki, Saori
Issue Date: 2008-07
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: IEEE International Conference on Research, Innovation and Vision for the Future, 2008. RIVF 2008.
Start page: 71
End page: 78
DOI: 10.1109/RIVF.2008.4586335
Abstract: Complex biological data generated from various experiments are stored in diverse data types in multiple datasets. By appropriately representing each biological dataset as a kernel matrix then combining them in solving problems, the kernel-based approach has become a spotlight in data integration and its application in bioinformatics and other fields as well. While linear combination of unweighed multiple kernels (UMK) is popular, there have been effort on multiple kernel learning (MKL) where optimal weights are learned by semi-definite programming or sequential minimal optimization (SMO-MKL). These methods provide high accuracy of biological prediction problems, but very complicated and hard to use, especially for non-experts in optimization. These methods are also usually of high computational cost and not suitable for large data sets. In this paper, we propose two simple but effective methods for determining weights for conic combination of multiple kernels. The former is to learn optimal weights formulated by our measure FSM for kernel matrix evaluation (feature space-based kernel matrix evaluation measure), denoted by FSM-MKL. The latter assigns a weight to each kernel that is proportional to the quality of the kernel, determining by direct cross validation, named proportionally weighted multiple kernels (PWMK). Experimental comparative evaluation of the four methods UMK, SMO-MKL, FSM-MKL and PWMK for the problem of protein-protein interactions shows that our proposed methods are simpler, more efficient but still effective. They achieved performances almost as high as that of MKL and higher than that of UMK.
Rights: Copyright (C) 2008 IEEE. Reprinted from IEEE International Conference on Research, Innovation and Vision for the Future, 2008. RIVF 2008., 71-78. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of JAIST's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
URI: http://hdl.handle.net/10119/8477
Material Type: publisher
Appears in Collections:a11-1. 会議発表論文 (Conference Papers)

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