Feature selection strategies for classifying high dimensional astronomical data sets

Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013

Volumn , Issue , 2013, Pages 35-41

  Donalek, Ciro ^a   Djorgovski, S G ^a   Mahabal, Ashish A ^a   Graham, Matthew J ^a   Drake, Andrew J ^a   Kumar, A Arun ^b   Philip, N Sajeeth ^b   Fuchs, Thomas J ^c   Turmon, Michael J ^c   Yang, Michael Ting Chang ^d   Longo, Giuseppe ^e  

^a CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^b ST THOMAS COLLEGE   (India)

^c CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^d NATIONAL CENTRAL UNIVERSITY   (Taiwan)

^e UNIVERSITY OF NAPLES FEDERICO II   (Italy)

Author keywords

astroinformatics; CRTS; feature selection; machine learning

Indexed keywords

BIG DATA; CATHODE RAY TUBES; DATA MINING; FEATURE EXTRACTION; LEARNING SYSTEMS; SURVEYS; TIME DOMAIN ANALYSIS;

ASTROINFORMATICS; ASTRONOMICAL DATA; HIGH-DIMENSIONAL; KEPLER MISSION; MULTI-DIMENSIONAL SPACE; OBJECT CLASSIFICATION; RESEARCH FRONTIERS; SCIENTIFIC FIELDS;

CLASSIFICATION (OF INFORMATION);

EID: 84893225157     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BigData.2013.6691731     Document Type: Conference Paper

References (21)

1. S.G. Djorgovski, Sky Surveys, Astronomical Techniques, Software, and Data, ed. H. Bond, Vol.2 of Planets, Stars, and Stellar Systems, Springer Verlag, 2012.
2. US Virtual Astronomical Observatory, http://www.usvao.org
3. S. Theodoridis, K. Koutroumbas, Pattern Recognition, AP, 2009.
4. A. J. Drake et al, "First Results from the Catalina Real-time Transient Survey," ApJ, 696, 870, 2009
5. Kepler Website, http://kepler.nasa.gov
6. J.W. Richards et al, "On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data," ApJ, Vol 733, Issue 1, 2011s
7. M. J. Graham, S.G. Djorgovski, A. Mahabal, C. Donalek, A. Drake, "Machine-Assisted discovery of relationship in Astronomy," Mon. Not. R. Astronom. Soc., 2013
8. J. Debosscher et al., "Automated supervised classification of variable stars," A&A, vol. 475, pp. 1159-1183, 2007
9. Caltech Time Series Characterization Service, http://nirgun.caltech.edu: 8000
10. R.S. Marko, K. Igor, "Theoretical and empirical analysis of Relief and ReliefF," Machine Learning Journal, 53:23-69, 2003.
11. Y. Sun, J. Li, "Iterative Relief for feature weighting," in proceedings of the 23rd International Conference on Machine Learning, 2006
12. K. Fukunaga, Introduction to Statistical Pattern Recognition, AP, 1990
13. J. G. Kohavi R., "Wrappers for feature subset selection," Artificial Intelligence, Vol.97, No.1-2, pp.272-324, 1997
14. L. J. Buturovic, "Improving K-nearest-neighbor density and error estimates," Pattern Recognition, Vol 26(4), pp 611-616, 1993
15. I. T. Nabney, Netlab. Algorithms for Pattern Recognition. Ed. Springer, 2001
16. L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone, "Classification and regression trees," Monterey, CA: Wadsworth &Brooks/Cole Advanced Books &Software, 1984
17. S.G. Djorgovski, A. A. Mahabal, C. Donalek, M. Graham, A. J. Drake, B. Moghaddam, M. Turmon, "Flashes in a Star Stream: Automated Classification of Astronomical Transient Events," in refereed proceedings of the IEEE eScience 2012 conference, 2012
18. C. Donalek et al, "New Approaches to Object Classification in Synoptic Sky Surveys," AIP Conf. Proc. 1082, 252-256, 2008
19. A. A. Mahabal et al, "Discovery, classification, and scientific exploration of transient events from the Catalina Real-Time Transient Survey," BASI, 39, 38, 2011
20. B. Sesar, in "RR Lyrae Stars, Metal-Poor Stars, and the Galaxy," ed. Mcwilliam, A. Carnegie Observatories Astrophysics Series, vol. 5, 135, 2011.
21. K. Kinemuchi, H.A. Smith, P.R. Wozniak, T.A. McKay, "Analysis of RR Lyrae Stars in the Northern Sky Variability Survey", The Astronomical Journal, 132, pp 1202-1220, 2006