Che et al. BMC Genomics 2010, 11(Suppl 2):S1 http://www.biomedcentral.com/1471-2164/11/S2/S1 Page 6 of 9 Figure 3 ROC curves for the “leave-one-feature-out” models using bagging For each dataset of Streptococcus, Staphylococcus, Salmonella, and all-three. ROC curves of the “leave-one-feature-out” models are calculated. any genome belonging to these three genera. For the GI detection of the other species, we provide a general model which was trained on three mixed datasets. The low decreasing of classification accuracy on the tests of all mixed datasets suggests that the models of different genera share a super GI structure model, which is fairly applicable to any other genomes. With more sequenced genomes available, we believe that more GIs will be stu- died, confirmed and assembled. To this end, our soft- ware package also provides a platform for users to build GI models for their own needs. Conclusions In this paper, we have presented a comparative study applying several machine learning algorithms for classi- fying the genomic island dataset. Our experimental results have shown that the J48 decision tree approach performed very well based on multiple performance eva- luation metrics. Furthermore, decision tree based ensemble algorithms were shown to improve the perfor- mance over the single decision tree algorithm. These results suggest that such decision tree based ensembles can be applied for genomic island classification.