Active Learning for Abstract Models of Collectives
Konferenz: ARCS 2015 - 28th International Conference on Architecture of Computing Systems
24.03.2015 - 27.03.2015 in Porto, Portugal
Tagungsband: ARCS 2015
Seiten: 4Sprache: EnglischTyp: PDFPersönliche VDE-Mitglieder erhalten auf diesen Artikel 10% Rabatt
Schiendorfer, Alexander; Anders, Gerrit; Reif, Wolfgang (Institute for Software & Systems Engineering, University of Augsburg, Germany)
Lassner, Christoph; Lienhart, Rainer (Multimedia Computing and Computer Vision Lab, University of Augsburg, Germany)
Organizational structures such as hierarchies provide an effective means to deal with the increasing complexity found in large-scale energy systems. In hierarchical systems, the concrete functions describing the subsystems can be replaced by abstract piecewise linear functions to speed up the optimization process. However, if the data points are weakly informative the resulting abstracted optimization problem introduces severe errors and exhibits bad runtime performance. Furthermore, obtaining additional point labels amounts to solving computationally hard optimization problems. Therefore, we propose to apply methods from active learning to search for informative inputs. We present first results experimenting with Decision Forests and Gaussian Processes that motivate further research. Using points selected by Decision Forests, we could reduce the average mean squared error of the abstract piecewise linear function by one third.