Effects of data prevalence on species distribution modelling using a genetic takagi-sugeno fuzzy system
暂无分享,去创建一个
[1] Shinji Fukuda. Assessing transferability of genetic algorithm-optimized fuzzy habitat preference models for Japanese medaka (Oryzias latipes) , 2010, 2010 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS).
[2] Hisao Ishibuchi,et al. Classification and modeling with linguistic information granules - advanced approaches to linguistic data mining , 2004, Advanced information processing.
[3] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[4] Shinji Fukuda,et al. Assessing the applicability of fuzzy neural networks for habitat preference evaluation of Japanese medaka (Oryzias latipes) , 2011, Ecol. Informatics.
[5] Bernard De Baets,et al. Interpretability-preserving genetic optimization of linguistic terms in fuzzy models for fuzzy ordered classification: An ecological case study , 2007, Int. J. Approx. Reason..
[6] Francisco Herrera,et al. Genetic fuzzy systems: taxonomy, current research trends and prospects , 2008, Evol. Intell..
[7] J. Elith,et al. Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models , 2009 .
[8] J. Lobo,et al. The effect of prevalence and its interaction with sample size on the reliability of species distribution models , 2009 .
[9] B. Baets,et al. DO ABSENCE DATA MATTER WHEN MODELLING FISH HABITAT PREFERENCE USING A GENETIC TAKAGI-SUGENO FUZZY MODEL? , 2012 .
[10] Shinji Fukuda,et al. A Preliminary Analysis for Improving Model Structure of Fuzzy Habitat Preference Model for Japanese Medaka (Oryzias latipes) , 2009, IFSA/EUSFLAT Conf..
[11] L. Belbin,et al. Evaluation of statistical models used for predicting plant species distributions: Role of artificial data and theory , 2006 .
[12] Ans Mouton,et al. Ecological relevance of' performance criteria for species distribution models , 2010 .
[13] Shinji Fukuda,et al. Assessing the effects of zero abundance data on habitat preference modelling using a genetic Takagi-Sugeno fuzzy model , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).
[14] Jane Elith,et al. The evaluation strip: A new and robust method for plotting predicted responses from species distribution models , 2005 .
[15] B. Baets,et al. Effect of model formulation on the optimization of a genetic Takagi–Sugeno fuzzy system for fish habitat suitability evaluation , 2011 .
[16] Bernard De Baets,et al. Knowledge-based versus data-driven fuzzy habitat suitability models for river management , 2009, Environ. Model. Softw..
[17] Bernard De Baets,et al. Prevalence-adjusted optimisation of fuzzy models for species distribution , 2009 .
[18] Francisco Herrera,et al. A Review of the Application of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions , 2013, IEEE Transactions on Fuzzy Systems.
[19] Shinji Fukuda. Effect of data quality on habitat preference evaluation for Japanese medaka (Oryzias latipes) using a simple genetic fuzzy system , 2010, International Conference on Fuzzy Systems.
[20] Hisao Ishibuchi,et al. Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems , 1997, Fuzzy Sets Syst..
[21] J. Nash,et al. River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .
[22] Fukuda Shinji,et al. Mathematical Characterization of Fuzziness in Fish Habitat Preference of Japanese Medaka (Oryzias latipes) in Agricultural Canal , 2005 .
[23] Michio Sugeno,et al. Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.
[24] F. Jiguet,et al. Selecting pseudo‐absences for species distribution models: how, where and how many? , 2012 .
[25] H. Ishibuchi. Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases , 2004 .
[26] Francisco Herrera,et al. Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures , 2011, Inf. Sci..
[27] Jesús Alcalá-Fdez,et al. Local identification of prototypes for genetic learning of accurate TSK fuzzy rule‐based systems , 2007, Int. J. Intell. Syst..
[28] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..
[29] Ning Xiong,et al. Evolutionary learning of rule premises for fuzzy modelling , 2001, Int. J. Syst. Sci..
[30] Shinji Fukuda,et al. Consideration of fuzziness: is it necessary in modelling fish habitat preference of Japanese medaka (Oryzias latipes)? , 2009 .
[31] Shinji Fukuda,et al. Effect of aggregation functions on the habitat preference modelling using a genetic Takagi-Sugeno fuzzy system , 2012, 2012 IEEE International Conference on Fuzzy Systems.
[32] Sébastien Brosse,et al. Dealing with Noisy Absences to Optimize Species Distribution Models: An Iterative Ensemble Modelling Approach , 2012, PloS one.
[33] Hisao Ishibuchi,et al. Application of parallel distributed genetics-based machine learning to imbalanced data sets , 2012, 2012 IEEE International Conference on Fuzzy Systems.
[34] R. Meentemeyer,et al. Equilibrium or not? Modelling potential distribution of invasive species in different stages of invasion , 2012 .
[35] Sovan Lek,et al. Selecting Variables for Habitat Suitability of Asellus (Crustacea, Isopoda) by Applying Input Variable Contribution Methods to Artificial Neural Network Models , 2010 .
[36] Shinji Fukuda,et al. Assessing Nonlinearity in Fish Habitat Preference of Japanese Medaka (Oryzias latipes) Using Genetic Algorithm-Optimized Habitat Prediction Models , 2008 .
[37] Truly Santika. Assessing the effect of prevalence on the predictive performance of species distribution models using simulated data , 2011 .
[38] Kazuaki Hiramatsu,et al. Prediction ability and sensitivity of artificial intelligence-based habitat preference models for predicting spatial distribution of Japanese medaka (Oryzias latipes) , 2008 .