ADEMA: An Algorithm to Determine Expected Metabolite Level Alterations Using Mutual Information
Figure 10
Comparison of ADEMA with other classifiers.
Figure shows the comparison of ADEMA's accuracy with other well-known non-linear classifiers. For PLS-DA, MetaboAnalyst's implementation is used, and for the rest of the techniques, WEKA implementations with default parameters are used. We report classification results for raw data and data that is normalized using the method described by Dubitzky et al [54]. Results show that ADEMA performs up to 31% better than the other methods, and performs better than all other methods in at least one dataset.