Machine learning model development for predicting road transport GHG emissions in Canada

Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-14010.1007/BF00058655Search in Google Scholar

Carletta, J. (1996). Assessing agreement on classification tasks: the kappa statistic. Computational linguistics, 22(2), 249-254.Search in Google Scholar

Dawson, C. W., & Wilby, R. (1998). An artificial neural network approach to rainfall-runoff modelling. Hydrological Sciences Journal, 43(1), 47-66.10.1080/02626669809492102Search in Google Scholar

Dietterich, T. G. (2000, June). Ensemble methods in machine learning. In International workshop on multiple classifier systems (pp. 1-15). Springer Berlin Heidelberg.10.1007/3-540-45014-9_1Search in Google Scholar

Environment and Climate Change Canada - Environmental Indicators - Greenhouse Gas Emissions. Retrieved May 29, 2017, from http://www.ec.gc.ca/indicateurs-indicators/default.asp?lang=En&n=FBF8455E-1Search in Google Scholar

Freund, Y., & Schapire, R. E. (1996, July). Experiments with a new boosting algorithm. In ICML (Vol. 96, pp. 148-156).Search in Google Scholar

Government of Canada, Environment and Climate Change Canada. (2017, April 13).Search in Google Scholar

Hosmer, D. W., & Lemeshow, S. (2000). Special topics. Applied Logistic Regression, Second Edition, 260-351.10.1002/0471722146.ch8Search in Google Scholar

Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). John Wiley & Sons.10.1002/9781118548387Search in Google Scholar

Hssina, B., Merbouha, A., Ezzikouri, H., & Erritali, M. (2014).A comparative study of decision tree ID3 and C4. 5. International Journal of Advanced Computer Science and Applications, 4(2), 13-19.10.14569/SpecialIssue.2014.040203Search in Google Scholar

Lang, H. (2013). Topics on Applied Mathematical Statistics. KTH Teknikvetenskap, version 0.97.Search in Google Scholar

S Lek Y S Park. (2008). Encyclopedia of Ecology | Multilayer Perceptron. Retrieved July 10, 201710.1016/B978-008045405-4.00162-2Search in Google Scholar

Mashaly, A. F., & Alazba, A. A. (2016). MLP and MLR models for instantaneous thermal efficiency prediction of solar still under hyper-arid environment. Computers and Electronics in Agriculture, 122, 146-155.10.1016/j.compag.2016.01.030Search in Google Scholar

McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.10.1007/BF02478259Search in Google Scholar

de Menezes, F. S., Liska, G. R., Cirillo, M. A., & Vivanco, M. J. (2017). Data classification with binary response through the Boosting algorithm and logistic regression. Expert Systems with Applications, 69, 62-73.10.1016/j.eswa.2016.08.014Open DOISearch in Google Scholar

Metz, B., Davidson, O. R., Bosch, P. R., & Dave, R. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 2007Search in Google Scholar

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Let a biogeography-based optimizer train your multi-layer perceptron. Information Sciences, 269, 188-209.10.1016/j.ins.2014.01.038Search in Google Scholar

Opitz, D. W., & Maclin, R. (1999). Popular ensemble methods: An empirical study. J. Artif. Intell. Res.(JAIR), 11, 169-198.10.1613/jair.614Open DOISearch in Google Scholar

Quinlan, J. R. (1993). C4. 5: Programming for machine learning. Morgan Kauffmann, 38.Search in Google Scholar

Peng, C. Y. J., Lee, K. L., & Ingersoll, G. M. (2002). An introduction to logistic regression analysis and reporting. The journal of educational research, 96(1), 3-14.10.1080/00220670209598786Search in Google Scholar

Sasaki, Y. (2007). The truth of the F-measure. Teach Tutor mater, 1(5).Search in Google Scholar

Sayad, S. (2011). Real time data mining. Canada: Self-Help Publishers.Search in Google Scholar

Shardlow, M. (2016). An analysis of feature selection techniques. The University of Manchester.Search in Google Scholar

Viera, A. J., & Garrett, J. M. (2005). Understanding interobserver agreement: the kappa statistic. Fam Med, 37(5), 360-363.15883903Search in Google Scholar

Wattimena, R. K. (2014). Predicting the stability of hard rock pillars using multinomial logistic regression. International journal of rock mechanics and mining sciences, 71, 33-40.10.1016/j.ijrmms.2014.03.015Search in Google Scholar

Werbos, P. J. (1974). Beyond regression: New tools for prediction and analysis in the behavioral sciences. Doctoral Dissertation, Applied Mathematics, Harvard University, MA.Search in Google Scholar

Winiwarter, W., & Rypdal, K. (2001). Assessing the uncertainty associated with national greenhouse gas emission inventories:: a case study for Austria. Atmospheric environment, 35(32), 5425-544010.1016/S1352-2310(01)00171-6Open DOISearch in Google Scholar

Zhou, Z. H. (2012). Ensemble methods: foundations and algorithms. CRC press.10.1201/b12207Search in Google Scholar

Yan, X., & Su, X. (2009). Linear regression analysis: theory and computing. World Scientific10.1142/6986Search in Google Scholar