A comparative study of ensemble learning methods for classification in bioinformatics

In this research work, we have proposed a novel ensemble learning approach “BBS method” which stands for Bagging, Boosting and Stacking with appropriate base classifiers for the classification of the five UCI datasets taken from the field of Bioinformatics. Experiments are conducted using Weka and Java Eclipse and it has been observed empirically that our approach gives better accuracy with lower root mean square error rate using the technique of ensemble learning. Henceforth we conclude that our proposed ensemble learning method is more suitable in handling the classification problem in the bioinformatics domain. Such approaches can be efficiently used in related real-life scenarios of classification domain.