Name
Abstract | ||
|---|---|---|
In most real-world information processing problems, data is not a free resource. Its acquisition is often expensive and time-consuming. We investigate how such cost factors can be included in supervised classification tasks by deriving classification as a sequential decision process and making it accessible to reinforcement learning. Depending on previously selected features and the internal belief of the classifier, a next feature is chosen by a sequential online feature selection that learns which features are most informative at each time step. Experiments on toy datasets and a handwritten digits classification task show significant reduction in required data for correct classification, while a medical diabetes prediction task illustrates variable feature cost minimization as a further property of our algorithm. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s13042-012-0092-x | Int. J. Machine Learning & Cybernetics |
Keywords | Field | DocType |
Reinforcement learning, Feature selection, Classification | Information processing,Feature selection,Pattern recognition,Computer science,Feature (computer vision),Minification,Artificial intelligence,Factor cost,Linear classifier,Classifier (linguistics),Machine learning,Reinforcement learning | Journal |
Volume | Issue | ISSN |
4 | 3 | 1868-808X |
Citations | PageRank | References |
7 | 0.44 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Rückstieß | 1 | 112 | 20.66 |
| Christian Osendorfer | 2 | 125 | 13.24 |
| Patrick van der Smagt | 3 | 188 | 24.23 |