Name
Title | ||
|---|---|---|
Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology. |
Abstract | ||
|---|---|---|
Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCBB.2017.2691329 | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Keywords | Field | DocType |
Machine learning algorithms,Data models,Training data,Training,Predictive models,Computational biology,Bioinformatics | Data modeling,Data mining,Predictive power,Swarm behaviour,Computer science,Robustness (computer science),Artificial intelligence,Computational biology,Computational learning theory,Ensemble learning,Particle swarm optimization,Online machine learning,Machine learning | Journal |
Volume | Issue | ISSN |
15 | 3 | 1545-5963 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro Alves | 1 | 0 | 1.35 |
| Shuang Liu | 2 | 0 | 0.34 |
| Daifeng Wang | 3 | 0 | 1.35 |
| Mark Gerstein | 4 | 8 | 6.72 |