A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics
This work addresses prediction challenges in genomics for researchers, but it is incremental as it applies existing ensemble methods to new datasets.
The paper tackled the problem of improving prediction accuracy in genomics by comparing ensemble classifiers, finding that the best methods offered statistically significant improvements over state-of-the-art approaches in genetic interaction and protein function prediction.
The combination of multiple classifiers using ensemble methods is increasingly important for making progress in a variety of difficult prediction problems. We present a comparative analysis of several ensemble methods through two case studies in genomics, namely the prediction of genetic interactions and protein functions, to demonstrate their efficacy on real-world datasets and draw useful conclusions about their behavior. These methods include simple aggregation, meta-learning, cluster-based meta-learning, and ensemble selection using heterogeneous classifiers trained on resampled data to improve the diversity of their predictions. We present a detailed analysis of these methods across 4 genomics datasets and find the best of these methods offer statistically significant improvements over the state of the art in their respective domains. In addition, we establish a novel connection between ensemble selection and meta-learning, demonstrating how both of these disparate methods establish a balance between ensemble diversity and performance.