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?
|Cite as: Tan, A.C. and Gilbert, D. (2003). An Empirical Comparison of Supervised Machine Learning Techniques in Bioinformatics. In Proc. First Asia-Pacific Bioinformatics Conference (APBC2003), Adelaide, Australia. CRPIT, 19. Chen, Y.-P. P., Ed. ACS. 219-222. |
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