Journal
2013 IEEE 13TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM)
Volume -, Issue -, Pages 807-816Publisher
IEEE
DOI: 10.1109/ICDM.2013.21
Keywords
Bioinformatics; Genomics; Supervised learning; Ensemble methods; Stacking; Ensemble selection
Categories
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available