STUDY COMPARISON BACKPROPOGATION, SUPPORT VECTOR MACHINE, AND EXTREME LEARNING MACHINE FOR BIOINFORMATICS DATA

umi mahdiyah, M. Isa Irawan, Elly Matul Imah

Abstract


A successful understanding on how to make computers learn would open up many new uses of computers and new levels of competence and customization. A detailed understanding on information- processing algorithms for machine learning might lead to a better understanding of human learning abilities and disabilities. There are many type of machine learning that we know, which includes Backpropagation (BP), Extreme Learning Machine (ELM), and Support Vector Machine (SVM). This research uses five data that have several characteristics. The result of this research is all the three investigated models offer comparable classification accuracies. This research has three type conclusions, the best performance in accuracy is BP, the best performance in stability is SVM and the best performance in CPU time is ELM for bioinformatics data.

Keywords


Machine Learning, Backpropagation, Extreme Learning Machine, Support Vector Machine, Bioinformatics

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DOI: http://dx.doi.org/10.21609/jiki.v8i1.284

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