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2003

An Empirical Comparison of Supervised Machine Learning Techniques in Bioinformatics

9 years 2 months ago
An Empirical Comparison of Supervised Machine Learning Techniques in Bioinformatics
Research in bioinformatics is driven by the experimental data. Current biological databases are populated by vast amounts of experimental data. Machine learning has been widely applied to bioinformatics and has gained a lot of success in this research area. At present, with various learning algorithms available in the literature, researchers are facing difficulties in choosing the best method that can apply to their data. We performed an empirical study on 7 individual learning systems and 9 different combined methods on 4 different biological data sets, and provide some suggested issues to be considered when answering the following questions: (i) How does one choose which algorithm is best suitable for their data set? (ii) Are combined methods better than a single approach? (iii) How does one compare the effectiveness of a particular algorithm to the others?
Aik Choon Tan, David Gilbert
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where APBC
Authors Aik Choon Tan, David Gilbert
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