Comparison of Binary Classification Based on Signed Distance Functions with Support Vector Machines

Details Comparison of Binary Classification Based on Signed Distance Functions with Support Vector Machines Comparison of Binary Classification Based on Signed Distance Functions with Support Vector Machines

2008-12-16

arxiv.org/abs/0812.3147v1

Computer Science

Learning

5 pages, 4 figures. Presented at the Ohio Collaborative Conference on Bioinformatics (OCCBIO), June 2006

Scientific paper

We investigate the performance of a simple signed distance function (SDF) based method by direct comparison with standard SVM packages, as well as K-nearest neighbor and RBFN methods. We present experimental results comparing the SDF approach with other classifiers on both synthetic geometric problems and five benchmark clinical microarray data sets. On both geometric problems and microarray data sets, the non-optimized SDF based classifiers perform just as well or slightly better than well-developed, standard SVM methods. These results demonstrate the potential accuracy of SDF-based methods on some types of problems.

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