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Original Articles

A new framework to deal with the class imbalance problem in urban gain modeling based on clustering and ensemble models

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Pages 5669-5692 | Received 17 Jan 2021, Accepted 05 Apr 2021, Published online: 16 Jun 2021

References

  • Ahmadlou M, Delavar MR, Basiri A, Karimi M. 2019. A comparative study of machine learning techniques to simulate land use changes. J Indian Soc Remote Sens. 47(1):53–62.
  • Basse RM, Omrani H, Charif O, Gerber P, Bódis K. 2014. Land use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale. Appl Geogr. 53:160–171.
  • Breiman L. 2001. Random forests. Machine Learn. 45(1):5–32.
  • Calinski T, Harabasz J. 1974. A dendrite method for cluster analysis. Comm Stats - Theory & Methods. 3(1):1–27.
  • Chawla NV. 2009. Data mining for imbalanced datasets: An overview. In: Data mining and knowledge discovery handbook. Boston (MA): Springer.
  • Cherkassky V, Mulier F. 1999. Vapnik-Chervonenkis (VC) learning theory and its applications. IEEE Trans Neural Networks. 10:985–987.
  • Davies DL, Bouldin DW. 1979. A cluster separation measure. IEEE Trans Pattern Anal Mach Intell. PAMI-1(2):224–227.
  • Defries R, Hansen A, Turner B, Reid R, Liu J. 2007. Land use change around protected areas: management to balance human needs and ecological function. Ecol Appl. 17(4):1031–1038.
  • Duran-Medina E, Mas JF, Velázquez A. 2005. Land use/cover change in community-based forest management regions and protected areas in Mexico. In: The Community Forests of Mexico: managing for Sustainable Landscapes. Austin (TX): University of Texas Press.
  • Efron B. 1987. Better bootstrap confidence intervals. J Am Stat Assoc. 82(397):171–185.
  • Feng W, Huang W, Ren J. 2018. Class imbalance ensemble learning based on the margin theory. Appl Sci. 8(5):815.
  • Fernandes E, DE Leon Ferreira ACP, Carvalho D, Yao X. 2019. Ensemble of classifiers based on multiobjective genetic sampling for imbalanced data. IEEE Trans Knowl Data Eng. 32(6):1104–1115.
  • Fernández A, DEL Río S, Chawla NV, Herrera F. 2017. An insight into imbalanced big data classification: outcomes and challenges. Complex Intell Syst. 3(2):105–120.
  • Guo H, Viktor HL. 2004. Learning from imbalanced data sets with boosting and data generation: the databoost-im approach. SIGKDD Explor Newsl. 6(1):30–39.
  • Halkidi M, Batistakis Y, Vazirgiannis M. 2001. On clustering validation techniques. J Intell Information Syst. 17(2/3):107–145.
  • He H, Garcia EA. 2009. Learning from imbalanced data. IEEE Trans Knowl Data Eng. 21:1263–1284.
  • Holland JH. 1992. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. Cambridge (MA): MIT press.
  • Huang B, Xie C, Tay R, Wu B. 2009. Land-use-change modeling using unbalanced support-vector machines. Environ Plann B Plann Des. 36(3):398–416.
  • Jain AK, Murty MN, Flynn PJ. 1999. Data clustering: a review. ACM Comput Surv. 31(3):264–323.
  • Japkowicz N. 2000. Learning from imbalanced data sets: a comparison of various strategies. In AAAI workshop on learning from imbalanced data sets, Menlo Park, CA, p. 10–15.
  • Karimi F, Sultana S, Babakan AS, Suthaharan S. 2019. An enhanced support vector machine model for urban expansion prediction. Comput Environ Urban Syst. 75:61–75.
  • Krawczyk B. 2016. Learning from imbalanced data: open challenges and future directions. Prog Artif Intell. 5(4):221–232.
  • Likas A, Vlassis N, Verbeek JJ. 2003. The global k-means clustering algorithm. Pattern Recognit. 36(2):451–461.
  • Liu RY. 1988. Bootstrap procedures under some non-iid models. Ann Statist. 16(4):1696–1708.
  • Liu Y, Li Z, Xiong H, Gao X, Wu J. 2010. Understanding of internal clustering validation measures. IEEE International Conference on Data Mining, 2010. IEEE, p. 911–916.
  • Long H, Qu Y. 2018. Land use transitions and land management: A mutual feedback perspective. Land Use Policy. 74:111–120.
  • Longadge R, Dongre S, Malik L. 2013. Class imbalance problem in data mining review. International Journal of Computer Science and Network. 2(1): 1–6.
  • Macqueen J. 1967. Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, p. 281–297.
  • Nagendra H, Munroe DK, Southworth J. 2004. From pattern to process: landscape fragmentation and the analysis of land use/land cover change. Agriculture, Ecosystems & Environment. 101(2): 111–115.
  • Oh S, Lee MS, Zhang B-T. 2010. Ensemble learning with active example selection for imbalanced biomedical data classification. IEEE/ACM Trans Comput Biol Bioinf. 8:316–325.
  • Oshiro TM, Perez PS, Baranauskas JA. 2012. How many trees in a random forest? International workshop on machine learning and data mining in pattern recognition. Berlin, Heidelberg: Springer. p. 154–168.
  • Pijanowski BC, Brown DG, Shellito BA, Manik GA. 2002. Using neural networks and GIS to forecast land use changes: a land transformation model. Comput Environ Urban Syst. 26(6):553–575.
  • Pontius RG, Castella J-C, DE Nijs T, Duan Z, Fotsing E, Goldstein N, Kok K, Koomen E, Lippitt CD, Mcconnell W. 2018. Lessons and challenges in land change modeling derived from synthesis of cross-case comparisons. In Trends in spatial analysis and modelling. Cham: Springer.
  • Pontius RG, Parmentier B. 2014. Recommendations for using the relative operating characteristic (ROC). Landscape Ecol. 29(3):367–382.
  • Pontius Jr RG, Si K. 2014. The total operating characteristic to measure diagnostic ability for multiple thresholds. Int J Geograph Inform Sci. 28(3):570–583.
  • Pontius RG, Walker R, Yao-Kumah R, Arima E, Aldrich S, Caldas M, Vergara D. 2007. Accuracy assessment for a simulation model of Amazonian deforestation. Ann Assoc Am Geograph. 97(4):677–695.
  • Qiang Y, Lam NS. 2015. Modeling land use and land cover changes in a vulnerable coastal region using artificial neural networks and cellular automata. Environ Monit Assess. 187(3):57.
  • Quintas-Soriano C, Castro AJ, Castro H, García-Llorente M. 2016. Impacts of land use change on ecosystem services and implications for human well-being in Spanish drylands. Land Use Policy. 54:534–548.
  • Rotem-Mindali O, Michael Y, Helman D, Lensky IM. 2015. The role of local land-use on the urban heat island effect of Tel Aviv as assessed from satellite remote sensing. Appl Geogr. 56:145–153.
  • Salunkhe UR, Mali SN. 2016. Classifier ensemble design for imbalanced data classification: a hybrid approach. Procedia Comput Sci. 85:725–732.
  • Samardžić‐Petrović M, Dragićević S, Kovačević M, Bajat B. 2016. Modeling urban land use changes using support vector machines. Trans in Gis. 20(5):718–734.
  • Samardžić-Petrović M, Kovačević M, Bajat B, Dragićević S. 2017. Machine learning techniques for modelling short term land-use change. IJGI. 6(12):387.
  • Shafizadeh-Moghadam H. 2019. Improving spatial accuracy of urban growth simulation models using ensemble forecasting approaches. Comput Environ Urban Syst. 76:91–100.
  • Shafizadeh-Moghadam H, Asghari A, Taleai M, Helbich M, Tayyebi A. 2017a. Sensitivity analysis and accuracy assessment of the land transformation model using cellular automata. GISci Remote Sens. 54(5):639–656.
  • Shafizadeh-Moghadam H, Tayyebi A, Ahmadlou M, Delavar MR, Hasanlou M. 2017b. Integration of genetic algorithm and multiple kernel support vector regression for modeling urban growth. Comput Environ Urban Syst. 65:28–40.
  • Sirami C, Caplat P, Popy S, Clamens A, Arlettaz R, Jiguet F, Brotons L, Martin JL. 2017. Impacts of global change on species distributions: obstacles and solutions to integrate climate and land use. Global Ecol Biogeogr. 26(4):385–394.
  • Smith P, House JI, Bustamante M, Sobocká J, Harper R, Pan G, West PC, Clark JM, Adhya T, Rumpel C, et al. 2016. Global change pressures on soils from land use and management. Glob Change Biol. 22(3):1008–1028.
  • Sun Y, Wong AK, Kamel MS. 2009. Classification of imbalanced data: A review. Int J Patt Recogn Artif Intell. 23(4):687–719.
  • Tayyebi A, Pijanowski BC. 2014. Modeling multiple land use changes using ANN, CART and MARS: Comparing tradeoffs in goodness of fit and explanatory power of data mining tools. Int J Appl Earth Obs Geoinf. 28:102–116.
  • Tayyebi A, Pijanowski BC, Linderman M, Gratton C. 2014. Comparing three global parametric and local non-parametric models to simulate land use change in diverse areas of the world. Environmental Modelling & Software. 59:202–221.
  • Tomich TP, Thomas DE, VAN Noordwijk M. 2004. Environmental services and land use change in Southeast Asia: from recognition to regulation or reward? Agricult Ecosyst Environ. 104(1):229–244.
  • Vapnik VN. 1999. An overview of statistical learning theory. IEEE Trans Neural Netw. 10(5):988–999.
  • Wang L. 2005. Support vector machines: theory and applications. Berlin, ‎Heidelberg: Springer Science & Business Media.
  • Wu Q, Li H-Q, Wang R-S, Paulussen J, He Y, Wang M, Wang B-H, Wang Z. 2006. Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landscape Urban Plann. 78(4):322–333.
  • Zhai J, Zhang S, Wang C. 2017. The classification of imbalanced large data sets based on mapreduce and ensemble of elm classifiers. Int J Mach Learn & Cyber. 8(3):1009–1017.

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