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
