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Effect of finite sample size on feature selection and classification: A simulation study

dc.contributor.authorWay, Ted W.
dc.contributor.authorSahiner, Berkman
dc.contributor.authorHadjiiski, Lubomir M.
dc.contributor.authorChan, Heang‐ping
dc.date.accessioned2017-01-06T20:46:30Z
dc.date.available2017-01-06T20:46:30Z
dc.date.issued2010-02
dc.identifier.citationWay, Ted W.; Sahiner, Berkman; Hadjiiski, Lubomir M.; Chan, Heang‐ping (2010). "Effect of finite sample size on feature selection and classification: A simulation study." Medical Physics 37(2): 907-920.
dc.identifier.issn0094-2405
dc.identifier.issn2473-4209
dc.identifier.urihttps://hdl.handle.net/2027.42/134839
dc.publisherWiley Periodicals, Inc.
dc.publisherAmerican Association of Physicists in Medicine
dc.subject.othersupport vector machines
dc.subject.othersample size effect
dc.subject.otherLaser Doppler velocimetry
dc.subject.otherComputer aided diagnosis
dc.subject.otherEigenvalues
dc.subject.otherLungs
dc.subject.otherMedical imaging
dc.subject.otherTesting procedures
dc.subject.otherRadiologists
dc.subject.otherComputed tomography
dc.subject.otherComputer software
dc.subject.otherPolynomials
dc.subject.otherComputerâ aided diagnosis
dc.subject.otherfeature extraction
dc.subject.otherGaussian distribution
dc.subject.otherimage classification
dc.subject.othermedical image processing
dc.subject.otherprincipal component analysis
dc.subject.othersupport vector machines
dc.subject.otherfeature selection
dc.subject.otherlinear discriminant analysis
dc.titleEffect of finite sample size on feature selection and classification: A simulation study
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.contributor.affiliationumDepartment of Radiology, University of Michigan, Ann Arbor, Michigan 48109â 5842
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/134839/1/mp4974.pdf
dc.identifier.doi10.1118/1.3284974
dc.identifier.sourceMedical Physics
dc.identifier.citedreferenceH. H. Barrett, C. K. Abbey, and E. Clarkson, â Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihoodâ generating functions,â J. Opt. Soc. Am. A Opt. Image Sci. Vis. 15, 1520 â 1535 ( 1998 ). 10.1364/JOSAA.15.001520 --> 1084-7529 -->
dc.identifier.citedreferenceB. Sahiner, H. P. Chan, and L. Hadjiiski, â Classifier performance prediction for computerâ aided diagnosis using a limited data set,â Med. Phys. MPHYA6 --> 0094-2405 --> 35, 1559 â 1570 ( 2008 ). 10.1118/1.2868757 -->
dc.identifier.citedreferenceB. Sahiner, H. P. Chan, and L. M. Hadjiiski, â Classifier performance estimation under the constraint of a finite sample size: Resampling schemes applied to neural network classifiers,â Neural Networks NNETEB --> 0893-6080 --> 21, 476 â 483 ( 2008 ). 10.1016/j.neunet.2007.12.012 -->
dc.identifier.citedreferenceQ. Li and K. Doi, â Analysis and minimization of overtraining effect in ruleâ based classifiers for computerâ aided diagnosis,â Med. Phys. MPHYA6 --> 0094-2405 --> 33, 320 â 328 ( 2006 ). 10.1118/1.1999126 -->
dc.identifier.citedreferenceQ. Li and K. Doi, â Comparison of typical evaluation methods for computerâ aided diagnostic schemes: Monte Carlo simulation study,â Med. Phys. MPHYA6 --> 0094-2405 --> 34, 871 â 876 ( 2007 ). 10.1118/1.2437130 -->
dc.identifier.citedreferenceS. V. Beiden, M. A. Maloof, and R. F. Wagner, â A general model for finiteâ sample effects in training and testing of competing classifiers,â IEEE Trans. Pattern Anal. Mach. Intell. ITPIDJ --> 0162-8828 --> 25, 1561 â 1569 ( 2003 ). 10.1109/TPAMI.2003.1251149 -->
dc.identifier.citedreferenceA. Jain and D. Zongker, â Feature selection: Evaluation, application, and small sample size performance,â IEEE Trans. Pattern Anal. Mach. Intell. ITPIDJ --> 0162-8828 --> 19, 153 â 158 ( 1997 ). 10.1109/34.574797 -->
dc.identifier.citedreferenceP. Pudil, J. Novovicová, and J. Kittler, â Floating search methods in feature selection,â Pattern Recogn. Lett. PRLEDG --> 0167-8655 --> 15, 1119 â 1125 ( 1994 ). 10.1016/0167-8655(94)90127-9 -->
dc.identifier.citedreferenceM. Kudo and J. Sklansky, â Comparison of algorithms that select features for pattern classifiers,â Pattern Recogn. PTNRA8 --> 0031-3203 --> 33, 25 â 41 ( 2000 ). 10.1016/S0031-3203(99)00041-2 -->
dc.identifier.citedreferenceC. Sima and E. R. Dougherty, â What should be expected from feature selection in smallâ sample settings,â Bioinformatics BOINFP --> 1367-4803 --> 22, 2430 â 2436 ( 2006 ). 10.1093/bioinformatics/btl407 -->
dc.identifier.citedreferenceJ. P. Hua, W. D. Tembe, and E. R. Dougherty, â Performance of featureâ selection methods in the classification of highâ dimension data,â Pattern Recogn. PTNRA8 --> 0031-3203 --> 42, 409 â 424 ( 2009 ). 10.1016/j.patcog.2008.08.001 -->
dc.identifier.citedreferenceJ. W. Lee, J. B. Lee, M. Park, and S. H. Song, â An extensive comparison of recent classification tools applied to microarray data,â Comput. Stat. Data Anal. CSDADW --> 0167-9473 --> 48, 869 â 885 ( 2005 ). 10.1016/j.csda.2004.03.017 -->
dc.identifier.citedreferenceX. G. Zhang, X. Lu, Q. Shi, X. Q. Xu, H. C. E. Leung, L. N. Harris, J. D. Iglehart, A. Miron, J. S. Liu, and W. H. Wong, â Recursive SVM feature selection and sample classification for massâ spectrometry and microarray data,â BMC Bioinf. BBMIC4 --> 1471-2105 --> 7, 13 ( 2006 ). 10.1186/1471-2105-7-13 -->
dc.identifier.citedreferenceJ. G. Dy and C. E. Brodley, â Feature selection for unsupervised learning,â J. Mach. Learn. Res. 1532-4435 --> 5, 845 â 889 ( 2004 ).
dc.identifier.citedreferenceS. Krishnan, K. Samudravijaya, and P. V. S. Rao, â Feature selection for pattern classification with Gaussian mixture models: A new objective criterion,â Pattern Recogn. Lett. PRLEDG --> 0167-8655 --> 17, 803 â 809 ( 1996 ). 10.1016/0167-8655(96)00047-5 -->
dc.identifier.citedreferenceR. Kohavi and G. H. John, â Wrappers for feature subset selection,â Artif. Intell. AINTBB --> 0004-3702 --> 97, 273 â 324 ( 1997 ). 10.1016/S0004-3702(97)00043-X -->
dc.identifier.citedreferenceL. Yu and H. Liu, â Efficient feature selection via analysis of relevance and redundancy,â J. Mach. Learn. Res. 1532-4435 --> 5, 1205 â 1224 ( 2004 ).
dc.identifier.citedreferenceK. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. ( Academic, New York, 1990 ).
dc.identifier.citedreferenceR. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis ( Wiley, New York, 1973 ).
dc.identifier.citedreferenceD. J. Hand, Discrimination and Classification ( Wiley, New York, 1981 ).
dc.identifier.citedreferenceT. W. Way, L. M. Hadjiiski, B. Sahiner, H. -P. Chan, P. N. Cascade, E. A. Kazerooni, N. Bogot, and C. Zhou, â Computerâ aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours,â Med. Phys. MPHYA6 --> 0094-2405 --> 33, 2323 â 2337 ( 2006 ). 10.1118/1.2207129 -->
dc.identifier.citedreferenceM. M. Galloway, â Texture classification using gray level run lengths,â Comput. Graph. Image Process. CGIPBG --> 0146-664X --> 4, 172 â 179 ( 1975 ). 10.1016/S0146-664X(75)80008-6 -->
dc.identifier.citedreferenceB. R. Dasarathy and E. B. Holder, â Image characterizations based on joint grayâ level runâ length distributions,â Pattern Recogn. Lett. PRLEDG --> 0167-8655 --> 12, 497 â 502 ( 1991 ). 10.1016/0167-8655(91)80014-2 -->
dc.identifier.citedreferenceT. Marill and D. Green, â On the effectiveness of receptors in recognition systems,â IEEE Trans. Inf. Theory IETTAW --> 0018-9448 --> 9, 11 â 17 ( 1963 ). 10.1109/TIT.1963.1057810 -->
dc.identifier.citedreferenceA. W. Whitney, â A direct method of nonparametric measurement selection,â IEEE Trans. Comput. ITCOB4 --> 0018-9340 --> C-20, 1100 â 1103 ( 1971 ). 10.1109/T-C.1971.223410 -->
dc.identifier.citedreferenceN. R. Draper, Applied Regression Analysis ( Wiley, New York, 1998 ).
dc.identifier.citedreferenceM. M. Tatsuoka, Multivariate Analysis, Techniques for Educational and Psychological Research, 2nd ed. ( Macmillan, New York, 1988 ).
dc.identifier.citedreferenceM. J. Norusis, SPSS for Windows Release 6 Professional Statistics ( SPSS, Chicago, 1993 ).
dc.identifier.citedreferenceS. D. Stearns, â On selecting features for pattern classifiers,â Third International Conference on Pattern Recognition, Coronado, CA, 1976, pp. 71 â 75.
dc.identifier.citedreferenceA. K. Jain, R. P. W. Duin, and J. Mao, â Statistical pattern recognition: A review,â IEEE Trans. Pattern Anal. Mach. Intell. ITPIDJ --> 0162-8828 --> 22, 4 â 37 ( 2000 ). 10.1109/34.824819 -->
dc.identifier.citedreferenceP. A. Lachenbruch, Discriminant Analysis ( Hafner, New York, 1975 ).
dc.identifier.citedreferenceC. J. C. Burges, â A tutorial on support vector machines for pattern recognition,â Data Min. Knowl. Discov. DMKDFD --> 1384-5810 --> 2, 121 â 167 ( 1998 ). 10.1023/A:1009715923555 -->
dc.identifier.citedreferenceY. Arzhaeva, M. Prokop, D. M. J. Tax, P. A. De Jong, C. M. Schaeferâ Prokop, and B. van Ginneken, â Computerâ aided detection of interstitial abnormalities in chest radiographs using a reference standard based on computed tomography,â Med. Phys. MPHYA6 --> 0094-2405 --> 34, 4798 â 4809 ( 2007 ). 10.1118/1.2795672 -->
dc.identifier.citedreferenceP. Campadelli, E. Casiraghi, and D. Artioli, â A fully automated method for lung nodule detection from posteroâ anterior chest radiographs,â IEEE Trans. Med. Imaging ITMID4 --> 0278-0062 --> 25, 1588 â 1603 ( 2006 ). 10.1109/TMI.2006.884198 -->
dc.identifier.citedreferenceA. K. Jerebko, J. D. Malley, M. Franaszek, and R. M. Summers, â Support vector machines committee classification method for computerâ aided polyp detection in CT colonography,â Acad. Radiol. 1076-6332 --> 12, 479 â 486 ( 2005 ). 10.1016/j.acra.2004.04.024 -->
dc.identifier.citedreferenceS. Ruping, â Incremental learning with support vector machines,â Proceedings of the IEEE International Conference on Data Mining, 2001, pp. 641 â 642.
dc.identifier.citedreferenceO. Chapelle, P. Haffner, and V. N. Vapnik, â Support vector machines for histogramâ based image classification,â IEEE Trans. Neural Netw. ITNNEP --> 1045-9227 --> 10, 1055 â 1064 ( 1999 ). 10.1109/72.788646 -->
dc.identifier.citedreferenceH. P. Chan, B. Sahiner, R. F. Wagner, and N. Petrick, â Classifier design for computerâ aided diagnosis: Effects of finite sample size on the mean performance of classical and neural network classifiers,â Med. Phys. MPHYA6 --> 0094-2405 --> 26, 2654 â 2668 ( 1999 ). 10.1118/1.598805 -->
dc.identifier.citedreferenceB. Sahiner, H. P. Chan, N. Petrick, R. F. Wagner, and L. M. Hadjiiski, â Feature selection and classifier performance in computerâ aided diagnosis: The effect of finite sample size,â Med. Phys. MPHYA6 --> 0094-2405 --> 27, 1509 â 1522 ( 2000 ). 10.1118/1.599017 -->
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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