STUDY COMPARISON BACKPROPOGATION, SUPPORT VECTOR MACHINE, AND EXTREME LEARNING MACHINE FOR BIOINFORMATICS DATA
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
Author: umi mahdiyah, M. Isa
Irawan, Elly Matul Imah
Journal Code: jptkomputergg150008
