{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T12:53:15Z","timestamp":1698670395432},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2009,2,12]],"date-time":"2009-02-12T00:00:00Z","timestamp":1234396800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2010,12]]},"DOI":"10.1007\/s10489-009-0166-y","type":"journal-article","created":{"date-parts":[[2009,2,11]],"date-time":"2009-02-11T18:41:27Z","timestamp":1234377687000},"page":"291-301","source":"Crossref","is-referenced-by-count":9,"title":["Applying correlation to enhance boosting technique using genetic programming as base learner"],"prefix":"10.1007","volume":"33","author":[{"given":"Luzia Vidal","family":"de Souza","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aurora","family":"Pozo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joel Mauricio Correa","family":"da\u00a0Rosa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anselmo Chaves","family":"Neto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2009,2,12]]},"reference":[{"key":"166_CR1","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-21606-5","volume-title":"The elements of statistical learning: data mining, inference, and prediction","author":"R Hastie","year":"2001","unstructured":"Hastie R, Tibshirani T, Friedman J (2001) The elements of statistical learning: data mining, inference, and prediction. Springer, Berlin"},{"key":"166_CR2","doi-asserted-by":"crossref","unstructured":"Todorovski L, Ljubic P, Dzeroski S (2004) Inducing polynomial equations for regression. In: Proceedings of the European conference on principles and practice of knowledge discovery in databases","DOI":"10.1007\/978-3-540-30115-8_41"},{"key":"166_CR3","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1613\/jair.199","volume":"3","author":"S Weiss","year":"1995","unstructured":"Weiss S, Inurkhya M (1995) Rule-based machine learning methods for functional predictions. J Artif Intell Res 3:383\u2013403","journal-title":"J Artif Intell Res"},{"key":"166_CR4","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55:119\u2013139","journal-title":"J Comput Syst Sci"},{"key":"166_CR5","unstructured":"Liu B, Mckay B, Abbrass HA (2003) Improving genetic classifiers with a booting algorithm. In: Proceedings of the congress on evolutionary computation"},{"key":"166_CR6","unstructured":"Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Proceedings of 13th international conference on machine learning, pp 148\u2013156"},{"key":"166_CR7","first-page":"172","volume":"31","author":"G Ridgeway","year":"1999","unstructured":"Ridgeway G (1999) The state of boosting. Comput Sci Stas 31:172\u2013181","journal-title":"Comput Sci Stas"},{"key":"166_CR8","first-page":"107","volume-title":"Proceedings of the 14th international conference on machine learning","author":"H Drucker","year":"1997","unstructured":"Drucker H (1997) Improving regressor using boosting. In: Proceedings of the 14th international conference on machine learning. Morgan Kaufmann, San Mateo, pp 107\u2013115"},{"key":"166_CR9","unstructured":"Solomatine D, Shrestha D (2004) Adaboost-rt: a boosting algorithm for regression problems. In: Proceedings of the IEEE international join conference on neural networks"},{"issue":"7","key":"166_CR10","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1162\/089976699300016106","volume":"11","author":"L Breiman","year":"1999","unstructured":"Breiman L (1999) Prediction games and arcing algorithms. Neural Comput 11(7):1493\u20131517","journal-title":"Neural Comput"},{"issue":"462","key":"166_CR11","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1198\/016214503000125","volume":"98","author":"P Buhlmann","year":"2003","unstructured":"Buhlmann P, Yu B (2003) Boosting with the l2 loss: Regression and classification. J Am Stat Assoc 98(462):324\u2013339","journal-title":"J Am Stat Assoc"},{"key":"166_CR12","first-page":"221","volume-title":"Advances in large margin classifiers","author":"L Mason","year":"1999","unstructured":"Mason L, Baxter J, Bartlett P, Frean M (1999) Functional gradient techniques for combining hypotheses. In: Advances in large margin classifiers. MIT Press, Cambridge, pp 221\u2013247"},{"key":"166_CR13","unstructured":"Assad M, Bone R (2003) Improving time series prediction by recurrent neural network ensembles. Universite de Tours, Tech Rep"},{"key":"166_CR14","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1023\/A:1008768404046","volume":"16","author":"MA Kaboudan","year":"2000","unstructured":"Kaboudan MA (2000) Genetic programming prediction on stock prices. J Comput Econ 16:207\u2013236","journal-title":"J Comput Econ"},{"issue":"3","key":"166_CR15","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1109\/TR.2007.903269","volume":"56","author":"EO Costa","year":"2007","unstructured":"Costa EO, de Souza GA, Pozo ATR, Vergilio SR (2007) Exploring genetic programming and boosting techniques to model software reliability. IEEE Trans Reliab 56(3):422\u2013434","journal-title":"IEEE Trans Reliab"},{"key":"166_CR16","volume-title":"Time series analysis: forecasting and control","author":"GEP Box","year":"1970","unstructured":"Box GEP, Jenkins GM (1970) Time series analysis: forecasting and control. Holden\u2013Day, Oakland"},{"key":"166_CR17","unstructured":"Quinlan J (1992) Learning with continuous classes. In: Proceedings of the 5th Australian joint conference on AI, pp 343\u2013348"},{"key":"166_CR18","volume-title":"Data mining. San Francisco","author":"I Witten","year":"2000","unstructured":"Witten I, Frank E (2000) Data mining. San Francisco. Morgan Kaufmann, San Mateo"},{"key":"166_CR19","volume-title":"Genetic programming: on the programming of computers by means of natural selection","author":"J Koza","year":"1992","unstructured":"Koza J (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge"},{"key":"166_CR20","volume-title":"Adaptation in natural and artificial systems","author":"J Holland","year":"1975","unstructured":"Holland J (1975) Adaptation in natural and artificial systems. MIT Press, Cambridge"},{"key":"166_CR21","doi-asserted-by":"crossref","DOI":"10.1007\/BFb0055923","volume-title":"Genetic programming: an introduction","author":"F Banzhaf","year":"1998","unstructured":"Banzhaf F, Nordin W, Keller P, Francone FD (1998) Genetic programming: an introduction. Morgan Kaufmann, San Mateo"},{"key":"166_CR22","first-page":"197","volume":"5","author":"R Schapire","year":"1990","unstructured":"Schapire R (1990) The strength of weak learnability. Mach Learn 5:197\u2013227","journal-title":"Mach Learn"},{"key":"166_CR23","first-page":"267","volume-title":"Selected papers from the 5th European conference on artificial evolution","author":"G Paris","year":"2002","unstructured":"Paris G, Robiliard D, Fonlupt C (2002) Applying boosting techniques to genetic programming. In: Selected papers from the 5th European conference on artificial evolution. Springer, London, pp 267\u2013280"},{"key":"166_CR24","series-title":"Springer Computer Science","first-page":"18","volume-title":"Proceedings of the international conference in Roanne","author":"R Bone","year":"2003","unstructured":"Bone R, Assad M, Crucianu M (2003) Boosting recurrent neural networks for times series prediction. In: Proceedings of the international conference in Roanne. Springer Computer Science, Springer, Roanne, pp 18\u201322"},{"key":"166_CR25","unstructured":"Iba H (1999). Bagging, boosting, and bloating in genetic programming. In: Proceedings of the genetic and evolutionary computation conference, pp 1053\u20131060"},{"key":"166_CR26","unstructured":"http:\/\/cran.r-project.org\/mirrors.html"},{"key":"166_CR27","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","volume":"11","author":"H Akaike","year":"1974","unstructured":"Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 11:716\u2013723","journal-title":"IEEE Trans Autom Control"},{"key":"166_CR28","volume-title":"Lil-gp 1.0 user\u2019s manual","author":"D Zongker","year":"1995","unstructured":"Zongker D, Punch B (1995) Lil-gp 1.0 user\u2019s manual. Michigan State University, East Lansing"},{"key":"166_CR29","volume-title":"Non-parametric statistics for the behavioural sciences","author":"S Siegal","year":"1988","unstructured":"Siegal S, Castellan N (1988) Non-parametric statistics for the behavioural sciences. McGraw\u2013Hill, New York"},{"key":"166_CR30","first-page":"1","volume":"7","author":"J Demsar","year":"2006","unstructured":"Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"key":"166_CR31","unstructured":"Souza GA, Vergilio SR (2006). Modeling software reliability growth with artificial neural networks. In: Proceedings of the IEEE Latin American test workshop, Buenos Aires, Argentina, pp 165\u2013170"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-009-0166-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-009-0166-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-009-0166-y","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,29]],"date-time":"2019-05-29T18:25:43Z","timestamp":1559154343000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-009-0166-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009,2,12]]},"references-count":31,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2010,12]]}},"alternative-id":["166"],"URL":"https:\/\/doi.org\/10.1007\/s10489-009-0166-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2009,2,12]]}}}