Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
A review of feature selection techniques in bioinformatics
Bioinformatics
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Analyzing Electroencephalograms Using Cloud Computing Techniques
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
Automatic phonocardiograph signal analysis for detecting heart valve disorders
Expert Systems with Applications: An International Journal
Bioinformatics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Cloud Technologies for Bioinformatics Applications
IEEE Transactions on Parallel and Distributed Systems
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Multiclass classification is an important technique to many complex biomedicine problems. Genetic algorithms (GA) are proven to be effective to select features prior to multiclass classification by support vector machines (SVM). However, their use is computation intensive. Based on SOA (Service Oriented Architecture) design principles, this paper proposes a cloud computing framework that exploits the inherent parallelism of GA-SVM classification to speed up the work. The performance evaluations on an mRNA benchmark cancer dataset have shown the effectiveness and efficiency of the framework. With a user-friendly web interface, the framework provides researchers an easy way to investigate the unrevealed secrets in the fast-growing repository of biomedical data.