{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:31:15Z","timestamp":1742995875272,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030319007"},{"type":"electronic","value":"9783030319014"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-31901-4_9","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:04:53Z","timestamp":1570662293000},"page":"74-82","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0120-4369","authenticated-orcid":false,"given":"Shikhar","family":"Srivastava","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2630-9172","authenticated-orcid":false,"given":"Fabian","family":"Eitel","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7115-0020","authenticated-orcid":false,"given":"Kerstin","family":"Ritter","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"9_CR1","unstructured":"Cattell, R.B.: Intelligence: Its Structure, Growth and Action, vol. 35. Elsevier (1987). https:\/\/psycnet.apa.org\/record\/1987-98151-000"},{"issue":"19","key":"9_CR2","doi-asserted-by":"publisher","first-page":"6829","DOI":"10.1073\/pnas.0801268105","volume":"105","author":"SM Jaeggi","year":"2008","unstructured":"Jaeggi, S.M., Buschkuehl, M., Jonides, J., Perrig, W.J.: Improving fluid intelligence with training on working memory. Proc. Natl. Acad. Sci. 105(19), 6829\u20136833 (2008). https:\/\/doi.org\/10.1073\/pnas.0801268105","journal-title":"Proc. Natl. Acad. Sci."},{"key":"9_CR3","doi-asserted-by":"publisher","first-page":"3","DOI":"10.3389\/neuro.01.003.2009","volume":"3","author":"E Ferrer","year":"2009","unstructured":"Ferrer, E., O\u2019Hare, E.D., Bunge, S.A.: Fluid reasoning and the developing brain. Front. Neurosci. 3, 3 (2009). https:\/\/doi.org\/10.3389\/neuro.01.003.2009","journal-title":"Front. Neurosci."},{"key":"9_CR4","doi-asserted-by":"publisher","DOI":"10.4324\/9781315804729","volume-title":"Analogical Reasoning in Children","author":"Usha Goswami","year":"2013","unstructured":"Goswami, U.: Analogical Reasoning in Children. Psychology Press (2013). https:\/\/doi.org\/10.4324\/9781315804729"},{"issue":"1","key":"9_CR5","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/S0160-2896(97)90014-3","volume":"24","author":"LS Gottfredson","year":"1997","unstructured":"Gottfredson, L.S.: Why g matters: the complexity of everyday life. Intelligence 24(1), 79\u2013132 (1997). https:\/\/doi.org\/10.1016\/S0160-2896(97)90014-3","journal-title":"Intelligence"},{"issue":"2","key":"9_CR6","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1017\/S0140525X07001185","volume":"30","author":"RE Jung","year":"2007","unstructured":"Jung, R.E., Haier, R.J.: The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence. Behav. Brain Sci. 30(2), 135\u2013154 (2007). https:\/\/doi.org\/10.1017\/S0140525X07001185","journal-title":"Behav. Brain Sci."},{"key":"9_CR7","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.intell.2015.04.009","volume":"51","author":"U Basten","year":"2015","unstructured":"Basten, U., Hilger, K., Fiebach, C.J.: Where smart brains are different: a quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence 51, 10\u201327 (2015). https:\/\/doi.org\/10.1016\/j.intell.2015.04.009","journal-title":"Intelligence"},{"key":"9_CR8","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.dcn.2018.03.001","volume":"32","author":"BJ Casey","year":"2018","unstructured":"Casey, B.J., et al.: The adolescent brain cognitive development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43\u201354 (2018). https:\/\/doi.org\/10.1016\/j.dcn.2018.03.001","journal-title":"Dev. Cogn. Neurosci."},{"key":"9_CR9","unstructured":"Adolescent Brain Cognitive Development (ABCD) Study. https:\/\/abcdstudy.org\/about\/"},{"key":"9_CR10","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017). https:\/\/doi.org\/10.1016\/j.media.2017.07.005","journal-title":"Med. Image Anal."},{"key":"9_CR11","doi-asserted-by":"publisher","unstructured":"Caruana, R., Munson, A., Niculescu-Mizil, A.: Getting the most out of ensemble selection. In: Sixth International Conference on Data Mining (ICDM 2006), pp. 828\u2013833. IEEE (2006). https:\/\/doi.org\/10.1109\/ICDM.2006.76","DOI":"10.1109\/ICDM.2006.76"},{"key":"9_CR12","unstructured":"Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. CoRR, abs\/1711.01468 (2017). http:\/\/arxiv.org\/abs\/1711.01468"},{"key":"9_CR13","doi-asserted-by":"publisher","DOI":"10.1201\/b12207","volume-title":"Ensemble Methods","author":"Zhi-Hua Zhou","year":"2012","unstructured":"Zhou, Z.-H.: Ensemble Methods: Foundations and Algorithms, 1st edn. Chapman & Hall\/CRC (2012). https:\/\/doi.org\/10.1201\/b12207. ISBN 1439830037, 9781439830031"},{"issue":"2","key":"9_CR14","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1023\/A:1022859003006","volume":"51","author":"LI Kuncheva","year":"2003","unstructured":"Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51(2), 181\u2013207 (2003). https:\/\/doi.org\/10.1023\/A:1022859003006","journal-title":"Mach. Learn."},{"key":"9_CR15","unstructured":"Sollich, P., Krogh, A.: Learning with ensembles: how overfitting can be useful. In: Advances in Neural Information Processing Systems, pp. 190\u2013196 (1996). http:\/\/papers.nips.cc\/paper\/1044-learning-with-ensembles-how-overfitting-can-be-useful.pdf"},{"issue":"4","key":"9_CR16","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1176\/appi.ajp.2017.17040469","volume":"175","author":"A Pfefferbaum","year":"2018","unstructured":"Pfefferbaum, A., et al.: Altered brain developmental trajectories in adolescents after initiating drinking. Am. J. Psychiatry 175(4), 370\u2013380 (2018). https:\/\/doi.org\/10.1176\/appi.ajp.2017.17040469. PMID: 29084454","journal-title":"Am. J. Psychiatry"},{"issue":"4","key":"9_CR17","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1111\/mono.12038","volume":"78","author":"N Akshoomoff","year":"2013","unstructured":"Akshoomoff, N., et al.: VIII. NIH toolbox cognition battery (CB): composite scores of crystallized, fluid, and overall cognition. Monogr. Soc. Res. Child Dev. 78(4), 119\u2013132 (2013). https:\/\/doi.org\/10.1111\/mono.12038","journal-title":"Monogr. Soc. Res. Child Dev."},{"issue":"4","key":"9_CR18","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","volume":"5","author":"B Krawczyk","year":"2016","unstructured":"Krawczyk, B.: Learning from imbalanced data: open challenges and future directions. Progress Artif. Intell. 5(4), 221\u2013232 (2016). https:\/\/doi.org\/10.1007\/s13748-016-0094-0","journal-title":"Progress Artif. Intell."},{"key":"9_CR19","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/j.neucom.2014.07.064","volume":"150","author":"J B\u0142aszczy\u0144ski","year":"2015","unstructured":"B\u0142aszczy\u0144ski, J., Stefanowski, J.: Neighbourhood sampling in bagging for imbalanced data. Neurocomputing 150, 529\u2013542 (2015). https:\/\/doi.org\/10.1016\/j.neucom.2014.07.064","journal-title":"Neurocomputing"},{"issue":"4","key":"9_CR20","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","volume":"42","author":"M Galar","year":"2012","unstructured":"Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., Herrera, F.: A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Tran. Syst. Man Cybern. Part C (Appl. Rev.) 42(4), 463\u2013484 (2012). https:\/\/doi.org\/10.1109\/TSMCC.2011.2161285","journal-title":"IEEE Tran. Syst. Man Cybern. Part C (Appl. Rev.)"},{"key":"9_CR21","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.asoc.2013.08.014","volume":"14","author":"B Krawczyk","year":"2014","unstructured":"Krawczyk, B., Wo\u017aniak, M., Schaefer, G.: Cost-sensitive decision tree ensembles for effective imbalanced classification. Appl. Soft Comput. 14, 554\u2013562 (2014). https:\/\/doi.org\/10.1016\/j.asoc.2013.08.014","journal-title":"Appl. Soft Comput."},{"issue":"3","key":"9_CR22","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1016\/j.neuroimage.2007.09.031","volume":"39","author":"DW Shattuck","year":"2008","unstructured":"Shattuck, D.W., et al.: Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39(3), 1064\u20131080 (2008). https:\/\/doi.org\/10.1016\/j.neuroimage.2007.09.031","journal-title":"Neuroimage"},{"issue":"2","key":"9_CR23","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.neuroimage.2007.09.031","volume":"41","author":"R Colom","year":"2013","unstructured":"Colom, R., et al.: Hippocampal structure and human cognition: key role of spatial processing and evidence supporting the efficiency hypothesis in females. Intelligence 41(2), 129\u2013140 (2013). https:\/\/doi.org\/10.1016\/j.neuroimage.2007.09.031","journal-title":"Intelligence"},{"key":"9_CR24","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"9_CR25","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1145\/1961189.1961199","volume":"2","author":"C-C Chang","year":"2011","unstructured":"Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011). https:\/\/doi.org\/10.1145\/1961189.1961199","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"issue":"1","key":"9_CR26","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001). https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach. Learn."},{"issue":"5","key":"9_CR27","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"Jerome H. Friedman","year":"2001","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189\u20131232 (2001). https:\/\/www.jstor.org\/stable\/2699986","journal-title":"The Annals of Statistics"},{"key":"9_CR28","doi-asserted-by":"publisher","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794. ACM (2016). https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"9_CR29","doi-asserted-by":"publisher","unstructured":"Whalen, S., Pandey, G.: A comparative analysis of ensemble classifiers: case studies in genomics. In: 2013 IEEE 13th International Conference on Data Mining, pp. 807\u2013816. IEEE (2013). https:\/\/doi.org\/10.1109\/ICDM.2013.21","DOI":"10.1109\/ICDM.2013.21"},{"issue":"4","key":"9_CR30","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1109\/TSE.2008.35","volume":"34","author":"S Lessmann","year":"2008","unstructured":"Lessmann, S., Baesens, B., Mues, C., Pietsch, S.: Benchmarking classification models for software defect prediction: a proposed framework and novel findings. IEEE Trans. Softw. Eng. 34(4), 485\u2013496 (2008). https:\/\/doi.org\/10.1109\/TSE.2008.35","journal-title":"IEEE Trans. Softw. Eng."},{"issue":"7084","key":"9_CR31","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1038\/nature04513","volume":"440","author":"P Shaw","year":"2006","unstructured":"Shaw, P., et al.: Intellectual ability and cortical development in children and adolescents. Nature 440(7084), 676 (2006). https:\/\/doi.org\/10.1038\/nature04513","journal-title":"Nature"}],"container-title":["Lecture Notes in Computer Science","Adolescent Brain Cognitive Development Neurocognitive Prediction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-31901-4_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:03:01Z","timestamp":1728518581000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-31901-4_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030319007","9783030319014"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-31901-4_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ABCD-NP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"abcdnp2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sibis.sri.com\/abcd-np-challenge\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"83% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The papers were written as a report to the submission for the challenge. All submissions were monitored by the organizers and the results were evaluated on a novel set of testing data. The papers were further reviewed by the chairs and organizers for quality assurance.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}