Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction
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Aertsen, 2010, Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests, Ecol. Modelling, 221, 1119, 10.1016/j.ecolmodel.2010.01.007
Asuncion, A., Newman, D.J., 2007. UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, CA,. Available from: 〈http://www.ics.uci.edu/~mlearn/MLRepository.html〉.
Atici, 2011, Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network, Expert Syst. Appl., 38, 9609, 10.1016/j.eswa.2011.01.156
Breiman, L., 1999. Using Adaptive Bagging to Debias Regressions. Technical Report No. 547 of University of California, Berkeley.
Chang, C.Z., 1997. Research on the Mix Proportion of High Flowing Eugenic Concrete. Chung Hua Univ., Hsin Chu, Taiwan.
Chang, 1996, A mix proportioning methodology for high-performance concrete, J. Chin. Inst. Eng., 19, 645, 10.1080/02533839.1996.9677830
Chou, 2012, Concrete compressive strength analysis using a combined classification and regression technique, Autom. Constr., 24, 52, 10.1016/j.autcon.2012.02.001
Chou, 2011, Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques, J. Comput. Civ. Eng., 25, 242, 10.1061/(ASCE)CP.1943-5487.0000088
Chung, F.C., 1995. Study on Characteristic of Coarse Aggregate in High-Performance Concrete. National Taiwan Univ. of Science and Technology. Taipei, Taiwan.
Erdal, 2013, Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms, J. Hydrol., 477, 119, 10.1016/j.jhydrol.2012.11.015
Erdal, 2013, High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform, Eng. Appl. Artif. Intell., 26, 1246, 10.1016/j.engappai.2012.10.014
Fazel-Zarandi, 2008, Fuzzy polynomial neural networks for approximation of the compressive strength of concrete, Appl. Soft Comput., 8, 488, 10.1016/j.asoc.2007.02.010
Friedman, 2001, Greedy function approximation, a gradient boosting machine, Ann. Stat., 29, 1189, 10.1214/aos/1013203451
Friedman, 2002, Stochastic gradient boosting, Comput. Stat. Data Anal., 38, 367, 10.1016/S0167-9473(01)00065-2
Garcia-Pedrajas, 2012, Supervised subspace projections for constructing ensembles of classifiers, Inf. Sci. (Ny), 193, 1, 10.1016/j.ins.2011.06.023
Giaccio, 1992, High-strength concretes incorporating different coarse aggregates, ACI Mater. J., 89, 242
Gjorv, 1990, Effect of condensed silica fume on the steel–concrete bond, ACI Mater. J., 87, 573
Gupta, 2006, Prediction of concrete strength using neural-expert system, J. Mater. Cıv. Eng., 18, 462, 10.1061/(ASCE)0899-1561(2006)18:3(462)
Hancock, 2005, A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies, Chemometrics Intell. Lab. Syst., 76, 185, 10.1016/j.chemolab.2004.11.001
Ho, T.K., 1995. Random decision forests. In: Proceedings of the Third International Conference on Document Analysis and Recognition. Montreal, Canada. pp. 278–282.
Ho, 1998, The random subspace method for constructing decision forests, IEEE Trans. Pattern Anal. Mach. Intell., 20, 832, 10.1109/34.709601
Hwang, G.E., 1991. A Study on Blast Furnace Slag Concrete. National Chiao Tung Univ. Hsin Chu, Taiwan.
Hwang, T.S., 1966. Compressive Strength of Blast Furnace Slag Concrete. National Chiao Tung Univ. Hsin Chu, Taiwan.
Kasperkiewicz, 1995, HPC strength prediction using artificial neural network, J. Comput. Civ. Eng., 9, 279, 10.1061/(ASCE)0887-3801(1995)9:4(279)
Lai, 2006, Random subspace method for multivariate feature selection, Pattern Recognition Lett., 27, 1067, 10.1016/j.patrec.2005.12.018
Langley, 1989, Structural concrete incorporating high volumes of ASTM class fly ash, ACI Mater. J., 86, 507
Lee, C.F., 1994. A Study on Dry Shrinkage and Creep Property of HPC. National Taiwan Univ. of Science and Technology. Taipei, Taiwan.
Lessard, 1993, Testing high-strength concrete compressive strength, ACI Mater. J., 90, 303
Lin, F.Y., 1994. Mixture Proportion and Quality of HPC. National Taiwan Univ. of Science and Technology. Taipei, Taiwan.
Louzada, 2011, Poly-bagging predictors for classification modelling for credit scoring, Expert Syst. Appl., 38, 12717, 10.1016/j.eswa.2011.04.059
Mert, A., Kilic, N., Akan, A., Evaluation of bagging ensemble method with time-domain feature extraction for diagnosing of arrhythmia beats. Neural Comput. Appl., http://dx.doi.org/10.1007/s00521-012-1232-7, in press.
Mo, H.L., 1995. A Study on High Performance Concrete. National Taiwan Univ. of Science and Technology. Taipei, Taiwan.
Pino-Mejias, 2008, Reduced bootstrap aggregating of learning algorithms, Pattern Recognition Lett., 29, 265, 10.1016/j.patrec.2007.10.002
Topcu, 2008, Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic, Comput. Mater. Sci., 41, 305, 10.1016/j.commatsci.2007.04.009
Wang, 2012, Two credit scoring models based on dual strategy ensemble trees, Knowledge-Based Syst., 26, 61, 10.1016/j.knosys.2011.06.020
Witten, 2005
Yeh, 1998, Modeling of strength of high-performance concrete using artificial neural networks, Cem. Concr. Res., 28, 1797, 10.1016/S0008-8846(98)00165-3
Yeh, 1999, Design of high-performance concrete mixture using neural networks and nonlinear programming, J. Comput. Civ. Eng., 13, 36, 10.1061/(ASCE)0887-3801(1999)13:1(36)
Yeh, 2009, Knowledge discovery of concrete material using genetic operation trees, Expert Syst. Appl., 36, 5807, 10.1016/j.eswa.2008.07.004