Name
Abstract | ||
|---|---|---|
In most real-world information processing problems, data is not a free resource; its acquisition is rather time-consuming and/or expensive. We investigate how these two factors can be included in supervised classification tasks by deriving classification as a sequential decision process and making it accessible to Reinforcement Learning. Our method performs a sequential feature selection that learns which features are most informative at each timestep, choosing the next feature depending on the already selected features and the internal belief of the classifier. Experiments on 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 |
|---|---|---|
2011 | 10.1007/978-3-642-25832-9_14 | Australasian Conference on Artificial Intelligence |
Keywords | Field | DocType |
sequential decision process,variable feature cost minimization,handwritten digits classification task,next feature,sequential feature selection,correct classification,medical diabetes prediction task,supervised classification task,required data,reinforcement learning,feature selection,classification | Information processing,Feature selection,Pattern recognition,Computer science,Feature (computer vision),Minification,Feature (machine learning),Artificial intelligence,Classifier (linguistics),Linear classifier,Machine learning,Reinforcement learning | Conference |
Volume | ISSN | Citations |
7106 | 0302-9743 | 7 |
PageRank | References | Authors |
0.67 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Rückstieß | 1 | 112 | 20.66 |
| Christian Osendorfer | 2 | 125 | 13.24 |
| Patrick van der Smagt | 3 | 188 | 24.23 |
| Thomas Rueckstiess | 4 | 7 | 0.67 |