| Machine learning feature selection methods for landslide susceptibility mapping N Micheletti, L Foresti, S Robert, M Leuenberger, A Pedrazzini, ... Mathematical geosciences 46, 33-57, 2014 | 267 | 2014 |
| Wildfire susceptibility mapping: Deterministic vs. stochastic approaches M Leuenberger, J Parente, M Tonini, MG Pereira, M Kanevski Environmental Modelling & Software 101, 194-203, 2018 | 131 | 2018 |
| Extreme Learning Machines for spatial environmental data M Leuenberger, M Kanevski Computers & Geosciences 85, 64-73, 2015 | 61 | 2015 |
| Geospatial approach for defining the Wildland-Urban Interface in the Alpine environment M Conedera, M Tonini, L Oleggini, CV Orozco, M Leuenberger, ... Computers, environment and urban systems 52, 10-20, 2015 | 45 | 2015 |
| The multipoint Morisita index for the analysis of spatial patterns J Golay, M Kanevski, CDV Orozco, M Leuenberger Physica A: Statistical Mechanics and its Applications 406, 191-202, 2014 | 35 | 2014 |
| Feature selection for regression problems based on the Morisita estimator of intrinsic dimension J Golay, M Leuenberger, M Kanevski Pattern Recognition 70, 126-138, 2017 | 31 | 2017 |
| Forest fires in a random forest M Leuenberger, M Kanevski, CD Vega Orozco EGU General Assembly Conference Abstracts, EGU2013-3238, 2013 | 16 | 2013 |
| Random Forest for toxicity of chemical emissions: features selection and uncertainty quantification A Marvuglia, M Leuenberger, M Kanevski, E Benetto Journal of Environmental Accounting and Management 3 (3), 229-241, 2015 | 10 | 2015 |
| Variables selection for ecotoxicity and human toxicity characterization using Gamma Test A Marvuglia, M Kanevski, M Leuenberger, E Benetto Computational Science and Its Applications–ICCSA 2014: 14th International …, 2014 | 8 | 2014 |
| Feature selection in environmental data mining combining Simulated Annealing and Extreme Learning Machine. M Leuenberger, MF Kanevski ESANN, 2014 | 7 | 2014 |
| Balancing a sample almost perfectly M Leuenberger, E Eustache, R Jauslin, Y Tillé Statistics & Probability Letters 180, 109229, 2022 | 4 | 2022 |
| Morisita-based feature selection for regression problems. J Golay, M Leuenberger, MF Kanevski ESANN, 2015 | 3 | 2015 |
| Feature selection for regression problems based on the morisita estimator of intrinsic dimension: Concept and case studies J Golay, M Leuenberger, MF Kanevski CoRR, abs/1602.00216, 2016 | 1 | 2016 |
| ADELE: Overview of a deep learning application for land use and land cover classification in Switzerland. M Leuenberger, A Meyer, G Milani, C Facchinetti Geophysical Research Abstracts 21, 2019 | | 2019 |
| Environmental Data Mining using Machine Learning Algorithms: Methodological Developments and Case Studies M Leuenberger Université de Lausanne, Faculté des géosciences et de l'environnement, 2018 | | 2018 |
| Study of Environmental Data Complexity using Extreme Learning Machine M Leuenberger, M Kanevski EGU General Assembly Conference Abstracts, 14015, 2017 | | 2017 |
| Wildfire susceptibility mapping: comparing deterministic and stochastic approaches M Pereira, M Leuenberger, J Parente, M Tonini EGU General Assembly Conference Abstracts, EPSC2016-7395, 2016 | | 2016 |
| Recent advances in environmental data mining M Leuenberger, M Kanevski EGU General Assembly Conference Abstracts, EPSC2016-6137, 2016 | | 2016 |
| Mapping of Estimations and Prediction Intervals Using Extreme Learning Machines M Leuenberger, M Kanevski EGU General Assembly Conference Abstracts, 6127, 2015 | | 2015 |
| Multivariate Mapping of Environmental Data Using Extreme Learning Machines M Leuenberger, M Kanevski EGU General Assembly Conference Abstracts, 4206, 2014 | | 2014 |
