caa a22 4500 445871474 CHVBK 20180317145513.0 cr unu---uuuuu 170323e20110101xx s 000 0 eng 10.1007/s10618-010-0194-6 doi (NATIONALLICENCE)springer-10.1007/s10618-010-0194-6 Detecting and ordering salient regions [Elektronische Daten] [Larry Shoemaker, Robert Banfield, Lawrence Hall, Kevin Bowyer, W. Kegelmeyer] We describe an ensemble approach to learning salient regions from arbitrarily partitioned data. The partitioning comes from the distributed processing requirements of large-scale simulations. The volume of the data is such that classifiers can train only on data local to a given partition. Since the data partition reflects the needs of the simulation, the class statistics can vary from partition to partition. Some classes will likely be missing from some or even most partitions. We combine a fast ensemble learning algorithm with scaled probabilistic majority voting in order to learn an accurate classifier from such data. Since some simulations are difficult to model without a considerable number of false positive errors, and since we are essentially building a search engine for simulation data, we order predicted regions to increase the likelihood that most of the top-ranked predictions are correct (salient). Results from simulation runs of a canister being torn and from a casing being dropped show that regions of interest are successfully identified in spite of the class imbalance in the individual training sets. Lift curve analysis shows that the use of data driven ordering methods provides a statistically significant improvement over the use of the default, natural time step ordering. Significant time is saved for the end user by allowing an improved focus on areas of interest without the need to conventionally search all of the data. The Author(s), 2010 Random forest nationallicence Saliency nationallicence Probabilistic voting nationallicence Imbalanced training data nationallicence Lift nationallicence Shoemaker Larry Computer Science and Engineering, University of South Florida, 33620-5399, Tampa, FL, USA aut Banfield Robert Computer Science and Engineering, University of South Florida, 33620-5399, Tampa, FL, USA aut Hall Lawrence Computer Science and Engineering, University of South Florida, 33620-5399, Tampa, FL, USA aut Bowyer Kevin Computer Science and Engineering, University of Notre Dame, 46556, South Bend, IN, USA aut Kegelmeyer W. Sandia National Labs, Computer and Information Sciences, P.O. Box 969, MS 9951, 94551, Livermore, CA, USA aut Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 22/1-2(2011-01-01), 259-290 1384-5810 22:1-2<259 2011 22 10618 https://doi.org/10.1007/s10618-010-0194-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10618-010-0194-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Shoemaker Larry Computer Science and Engineering, University of South Florida, 33620-5399, Tampa, FL, USA aut NATIONALLICENCE 700 1- Banfield Robert Computer Science and Engineering, University of South Florida, 33620-5399, Tampa, FL, USA aut NATIONALLICENCE 700 1- Hall Lawrence Computer Science and Engineering, University of South Florida, 33620-5399, Tampa, FL, USA aut NATIONALLICENCE 700 1- Bowyer Kevin Computer Science and Engineering, University of Notre Dame, 46556, South Bend, IN, USA aut NATIONALLICENCE 700 1- Kegelmeyer W. Sandia National Labs, Computer and Information Sciences, P.O. Box 969, MS 9951, 94551, Livermore, CA, USA aut NATIONALLICENCE 773 0- Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 22/1-2(2011-01-01), 259-290 1384-5810 22:1-2<259 2011 22 10618 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer