Mine classification with imbalanced data

IEEE Geoscience and Remote Sensing Letters

Volumn 6, Issue 3, 2009, Pages 528-532

  Williams, David P ^a   Myers, Vincent ^b   Silvious, Miranda Schatten ^c  

^a NATO UNDERSEA RESEARCH CENTRE   (Italy)

^b DEFENCE RESEARCH AND DEVELOPMENT CANADA   (Canada)

^c NIGHT VISION AND ELECTRONIC SENSORS DIRECTORATE   (United States)

Author keywords

Classification; Imbalanced data; Land mines; Logistic regression (LR); Mine detection; Radar; Sonar; Underwater mines

Indexed keywords

CLASSIFICATION; IMBALANCED DATA; LAND MINES; LOGISTIC REGRESSION (LR); MINE DETECTION; UNDERWATER MINES;

BOMBS (ORDNANCE); ELECTRIC REACTORS; LOGISTICS; MINES; MINING; REGRESSION ANALYSIS; REMOTE SENSING; SET THEORY; SONAR; TRACKING RADAR; UNDERWATER ACOUSTICS;

CLASSIFICATION (OF INFORMATION);

EID: 67651155767     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2009.2021964     Document Type: Article

References (12)

1. A. Owen, "Infinitely imbalanced logistic regression," J. Mach. Learn. Res., vol. 8, pp. 761-773, May 2007.
2. L. Bruzzone and S. Serpico, "Classification of imbalanced remote-sensing data by neural networks," Pattern Recognit. Lett., vol. 18, no. 11-13, pp. 1323-1328, Nov. 1997.
3. M. Kubat, R. Holte, and S. Matwin, "Machine learning for the detection of oil spills in satellite radar images," Mach. Learn., vol. 30, no. 2/3, pp. 195-215, Feb./Mar. 1998.
4. A. Dempster, N. Laird, and D. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Stat. Soc., B, vol. 39, no. 1, pp. 1-38, 1977.
5. N. Nasios and A. Bors, "Variational expectation-maximization training for Gaussian networks," in Proc. IEEE Workshop Neural Netw. Signal Process., 2003, pp. 339-348.
6. M. Beal and Z. Ghahramani, "The variational Bayesian EM algorithm for incomplete data: Application to scoring graphical model structures," Bayesian Stat., vol. 7, pp. 453-464, 2003.
7. V. Myers and ø. Midtgaard, "Fusion of contacts in synthetic aperture sonar imagery using performance estimates," in IOA Int. Conf. Detect. Classif. Underwater Targets, 2007, pp. 77-88.
8. V. Myers, "Sonar image segmentation using iteration and fuzzy logic," in Proc. CAD/CAC Conf., 2001.
9. J. Fawcett and V. Myers, "Computer-aided classification for a database of images of minelike objects," Defence R&D Canada-Atlantic, Dartmouth, NS, Canada, Tech. Rep. DRDC Atlantic TM 2004-272, 2005.
10. J. Hanley and B. McNeil, "The meaning and use of the area under a receiver operating characteristic (ROC) curve," Radiology, vol. 143, pp. 29-36, 1982.
11. L. Beran and D. Oldenburg, "Selecting a discrimination algorithm for unexploded ordnance remediation," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 9, pp. 2547-2557, Sep. 2008.
12. Q. Liu, X. Liao, and L. Carin, "Detection of unexploded ordnance via efficient semi-supervised and active learning," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 9, pp. 2558-2567, Sep. 2008.