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Author Romano, Raquel A. ♦ Aragon, Cecilia R. ♦ Ding, Chris
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Novel Application ♦ Positive Example ♦ Astronomical Imagery ♦ Corrupt Imagery ♦ Support Vector Machine ♦ Potential Supernova ♦ Supervised Learning Application ♦ Geometric Feature ♦ Multiple Svms ♦ High Level ♦ Unseen Test Data ♦ High Positive Classification Rate ♦ Feature Uncertainty ♦ High Accuracy ♦ Log Transform ♦ Largescale Supernova Survey ♦ Svm Decision Value ♦ Negative Data Set ♦ Present Crossvalidation Result ♦ Burdensome Workload ♦ Peaked Distribution ♦ Imbalanced Data Problem ♦ Negative Example ♦ Improved Supernova Recognition ♦ Heavy-tailed Distribution ♦ False Positive
Description We introduce a novel application of Support Vector Machines (SVMs) to the problem of identifying potential supernovae using photometric and geometric features computed from astronomical imagery. The challenges of this supervised learning application are significant: 1) noisy and corrupt imagery resulting in high levels of feature uncertainty, 2) features with heavy-tailed, peaked distributions, 3) extremely imbalanced and overlapping positive and negative data sets, and 4) the need to reach high positive classification rates, i.e. to find all potential supernovae, while reducing the burdensome workload of manually examining false positives. High accuracy is achieved via a sign-preserving, shifted log transform applied to features with peaked, heavy-tailed distributions. The imbalanced data problem is handled by oversampling positive examples, selectively sampling misclassified negative examples, and iteratively training multiple SVMs for improved supernova recognition on unseen test data. We present crossvalidation results and demonstrate the impact on a largescale supernova survey that currently uses the SVM decision value to rank-order 600,000 potential supernovae each night. 1.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2006-01-01
Publisher Institution Proceedings of the 5th International Conference of Machine Learning Applications