On Improving the Performance of Promoter Prediction Classifier for Eukaryotes Using Fuzzy Based Distribution Balanced Stratified Method

In the field of molecular biology, identifying eukaryotic promoters computationally is a demanding task. To improve the accuracy of a classifier, an effort is made in this paper to apply fuzzy based distribution balanced stratified `n´ fold cross validation technique on a promoter classifier. This technique is applied with both the artificial neural network and support vector machine classifiers. It is evaluated on a data set of human promoters and non-promoters and is found that the accuracy is improved considerably. This proposal also makes it possible to identify the rogue patterns. This greatly enhances the way to trace the specific functional and structural properties of these support vectors which may reveal some strong signals.