Astronomy and Astrophysics – Astrophysics
Scientific paper
2006-05-03
Astronomy and Astrophysics
Astrophysics
30 pages, 17 figures Accepted ApJS, March 27,2006
Scientific paper
10.1086/504801
Pattern recognition techniques have been used with increasing success for coping with the tremendous amounts of data being generated by automated surveys. Usually this process involves construction of training sets, the typical examples of data with known classifications. Given a feature set, along with the training set, statistical methods can be employed to generate a classifier. The classifier is then applied to process the remaining data. Feature set selection, however, is still an issue. This report presents techniques developed for accommodating data for which a substantive portion of the training set cannot be classified unambiguously, a typical case for low resolution data. Significance tests on the sort-ordered, sample-size normalized vote distribution of an ensemble of decision trees is introduced as a method of evaluating relative quality of feature sets. The technique is applied to comparing feature sets for sorting a particular radio galaxy morphology, bent-doubles, from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) database. Also examined are alternative functional forms for feature sets. Associated standard deviations provide the means to evaluate the effect of the number of folds, the number of classifiers per fold, and the sample size on the resulting classifications.
No associations
LandOfFree
Comparing Pattern Recognition Feature Sets for Sorting Triples in the FIRST Database does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Comparing Pattern Recognition Feature Sets for Sorting Triples in the FIRST Database, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Comparing Pattern Recognition Feature Sets for Sorting Triples in the FIRST Database will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-308130