{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T11:00:16Z","timestamp":1775473216842,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2011,4,23]],"date-time":"2011-04-23T00:00:00Z","timestamp":1303516800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2011,11]]},"DOI":"10.1007\/s11548-011-0559-3","type":"journal-article","created":{"date-parts":[[2011,4,22]],"date-time":"2011-04-22T04:27:08Z","timestamp":1303446428000},"page":"821-828","source":"Crossref","is-referenced-by-count":62,"title":["Investigating machine learning techniques for MRI-based classification of brain neoplasms"],"prefix":"10.1007","volume":"6","author":[{"given":"Evangelia I.","family":"Zacharaki","sequence":"first","affiliation":[]},{"given":"Vasileios G.","family":"Kanas","sequence":"additional","affiliation":[]},{"given":"Christos","family":"Davatzikos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2011,4,23]]},"reference":[{"issue":"1","key":"559_CR1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/S0022-510X(00)00274-4","volume":"175","author":"RA Prayson","year":"2000","unstructured":"Prayson RA, Agamanolis DP, Cohen ML, Estes ML (2000) Interobserver reproducibility among neuropathologists and surgical pathologists in fibrillary astrocytoma grading. J Neurol Sci 175(1): 33\u201339","journal-title":"J Neurol Sci"},{"issue":"1","key":"559_CR2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1002\/mrm.10315","volume":"49","author":"AR Tate","year":"2003","unstructured":"Tate AR et\u00a0al (2003) Automated classification of short echo time in in vivo 1H brain tumor spectra: a multicenter study. Magn Reson Med 49(1): 29\u201336","journal-title":"Magn Reson Med"},{"issue":"10","key":"559_CR3","first-page":"1696","volume":"25","author":"C Majos","year":"2004","unstructured":"Majos C et\u00a0al (2004) Brain tumor classification by proton MR spectroscopy: comparison of diagnostic accuracy at short and long TE. Am J Neuroradiol 25(10): 1696\u20131704","journal-title":"Am J Neuroradiol"},{"issue":"1","key":"559_CR4","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1002\/sim.1321","volume":"22","author":"Y Huang","year":"2003","unstructured":"Huang Y, Lisboa PJG, El-Deredy W (2003) Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection. Stat Med 22(1): 147\u2013164","journal-title":"Stat Med"},{"issue":"3","key":"559_CR5","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1109\/TITB.2006.889702","volume":"11","author":"C Lu","year":"2007","unstructured":"Lu C, Devos A, Suykens JAK, Arus C, Huffel S Van (2007) Bagging linear sparse Bayesian learning models for variable selection in cancer diagnosis. IEEE Trans Inf Technol Med 11(3): 338\u2013346","journal-title":"IEEE Trans Inf Technol Med"},{"issue":"3","key":"559_CR6","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.compbiomed.2004.11.003","volume":"36","author":"G Li","year":"2006","unstructured":"Li G, Yang J, Ye C, Geng D (2006) Degree prediction of malignancy in brain glioma using support vector machines. Comput Biol Med 36(3): 313\u2013325","journal-title":"Comput Biol Med"},{"issue":"2","key":"559_CR7","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.jmr.2004.12.007","volume":"173","author":"A Devos","year":"2005","unstructured":"Devos A et\u00a0al (2005) The use of multivariate MR imaging intensities versus metabolic data from MR spectroscopic imaging for brain tumour classification. J Magn Reson 173(2): 218\u2013228","journal-title":"J Magn Reson"},{"key":"559_CR8","unstructured":"Rajendran P, Madheswaran M (2009) An improved image mining technique for brain tumor classification using efficient classifier. Int J Comput Inf Secur 6(3)"},{"key":"559_CR9","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1002\/mrm.22147","volume":"62","author":"EI Zacharaki","year":"2009","unstructured":"Zacharaki EI, Wang S, Chawla S, Yoo DS, Wolf R, Melhem ER, Davatzikos C (2009) Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 62: 1609\u20131618","journal-title":"Magn Reson Med"},{"key":"559_CR10","doi-asserted-by":"crossref","unstructured":"Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: An update. SIGKDD Explor 11(1)","DOI":"10.1145\/1656274.1656278"},{"issue":"4","key":"559_CR11","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TKDE.2005.66","volume":"17","author":"H Liu","year":"2005","unstructured":"Liu H, Yu L (2005) Towards integrating feature selection algorithm for classification and clustering. IEEE Trans Knowl Data Eng 17(4): 491\u2013502","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"559_CR12","unstructured":"Hall MA (2000) Correlation-based feature selection for discrete and numeric class machine learning. In: 17th international conference on machine learning (ICML):359\u2013366"},{"key":"559_CR13","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0004-3702(03)00079-1","volume":"151","author":"M Dash","year":"2003","unstructured":"Dash M, Liu H (2003) Consistency-based search in feature selection. Artif Intell 151: 155\u2013176","journal-title":"Artif Intell"},{"key":"559_CR14","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","volume":"97","author":"R Kohavi","year":"1997","unstructured":"Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97: 273\u2013324","journal-title":"Artif Intell"},{"key":"559_CR15","volume-title":"Theory of psychological measurement","author":"EE Ghiselli","year":"1964","unstructured":"Ghiselli EE (1964) Theory of psychological measurement. McGraw-Hill Book Co, New York"},{"key":"559_CR16","unstructured":"Xu L, Yan P, Chang T (1988) Best first strategy for feature selection. In: 9th international conference on pattern recognition, pp 706\u2013708"},{"key":"559_CR17","doi-asserted-by":"crossref","unstructured":"Caruana R, Freitag D (1994) Greedy attribute selection. In: 11th international conference on machine learning, pp 28\u201336","DOI":"10.1016\/B978-1-55860-335-6.50012-X"},{"key":"559_CR18","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4615-0337-8","volume-title":"Scatter search: methodology and implementations C","author":"M Laguna","year":"2003","unstructured":"Laguna M, Mart R (2003) Scatter search: methodology and implementations C. Kluwer, Dordrecht"},{"issue":"3","key":"559_CR19","first-page":"291","volume":"3","author":"GM Gandhi","year":"2010","unstructured":"Gandhi GM, Srivatsa SK (2010) Adaptive machine learning algorithm (AMLA) using J48 classifier for an NIDS environment. Adv Comput Sci Technol 3(3): 291\u2013304","journal-title":"Adv Comput Sci Technol"},{"key":"559_CR20","first-page":"21","volume":"13","author":"TM Cover","year":"1967","unstructured":"Cover TM, Hart PE (1967) Nearest neighbor pattern classification. Inst Electr Electron Eng Trans Inf Theory 13: 21\u201327","journal-title":"Inst Electr Electron Eng Trans Inf Theory"},{"key":"559_CR21","doi-asserted-by":"crossref","unstructured":"Demiroz G, Guvenir A (1997) Classification by voting feature intervals. In: 9th European conference on machine learning, pp 85\u201392","DOI":"10.1007\/3-540-62858-4_74"},{"key":"559_CR22","unstructured":"Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machines. Software available at http:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvm"},{"key":"559_CR23","unstructured":"John GH, Langley P (1995) Estimating continuous distributions in bayesian classifiers. In: 11th conference on uncertainty in artificial intelligence, San Mateo, pp 338\u2013345"},{"key":"559_CR24","doi-asserted-by":"crossref","unstructured":"Zacharaki EI, Wang S, Chawla S, Yoo DS, Wolf R, Melhem ER, Davatzikos C (2009) MRI-based classification of brain tumor type and grade using SVM-RFE. In: 6th IEEE International Symposis Biomedical Imaging (ISBI 2009), Boston, Massachusetts, USA","DOI":"10.1109\/ISBI.2009.5193232"},{"key":"559_CR25","doi-asserted-by":"crossref","first-page":"4365","DOI":"10.1109\/ICMLC.2005.1527706","volume":"7","author":"Y-M Huang","year":"2005","unstructured":"Huang Y-M, Du S-X (2005) weighted support vector machine for classification with uneven training class sizes. Int Conf Mach Learn Cybern 7: 4365\u20134369","journal-title":"Int Conf Mach Learn Cybern"},{"key":"559_CR26","volume-title":"Principal component analysis, series: springer series in statistics","author":"IT Jolliffe","year":"2002","unstructured":"Jolliffe IT (2002) Principal component analysis, series: springer series in statistics, 2nd edn. Springer, NY","edition":"2"},{"key":"559_CR27","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1148\/radiol.2432060493","volume":"243","author":"RN Al-Okaili","year":"2007","unstructured":"Al-Okaili RN, Krejza J, Woo JH, Wolf RL, O\u2019Rourke DM, Judy KD, Poptani H, Melhem ER (2007) Intraaxial brain masses: MR imaging\u2013based diagnostic strategy\u2014initial experience. Radiology 243: 539\u2013550","journal-title":"Radiology"},{"issue":"2","key":"559_CR28","first-page":"214","volume":"25","author":"MH Lev","year":"2004","unstructured":"Lev MH et\u00a0al (2004) Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas. Am J Neuroradiol 25(2): 214\u2013221","journal-title":"Am J Neuroradiol"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-011-0559-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11548-011-0559-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-011-0559-3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,10]],"date-time":"2019-06-10T02:28:52Z","timestamp":1560133732000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11548-011-0559-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,4,23]]},"references-count":28,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2011,11]]}},"alternative-id":["559"],"URL":"https:\/\/doi.org\/10.1007\/s11548-011-0559-3","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2011,4,23]]}}}