Name
Abstract | ||
|---|---|---|
We will introduce the problem of classification in large cohort studies containing heterogeneous data. The data in a cohort study comes in separate groups, which can be turned on or off. Each group consists of data coming from one specific measurement instrument. We provide a "cross-sectional" investigation on this data to see the relative power of the different groups. We also propose a way of improving on the classification performance in individual cohort studies using other cohort studies by using an intuitive workflow approach. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-662-45231-8_32 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
meta-analysis,machine learning,data mining,classification,feature selection,cohort studies | Data mining,Disjoint sets,Feature selection,Computer science,Meta-analysis,Cohort study,Workflow | Conference |
Volume | ISSN | Citations |
8803 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan K. Vis | 1 | 0 | 0.34 |
| Joost N. Kok | 2 | 1429 | 121.49 |