IFROWANN: imbalanced fuzzy-rough ordered weighted average nearest neighbor classification E Ramentol, S Vluymans, N Verbiest, Y Caballero, R Bello, C Cornelis, ... IEEE Transactions on Fuzzy Systems 23 (5), 1622-1637, 2014 | 71 | 2014 |

Multiple instance learning F Herrera, S Ventura, R Bello, C Cornelis, A Zafra, D Sánchez-Tarragó, ... Multiple instance learning, 17-33, 2016 | 70 | 2016 |

Fuzzy rough classifiers for class imbalanced multi-instance data S Vluymans, DS Tarragó, Y Saeys, C Cornelis, F Herrera Pattern Recognition 53, 36-45, 2016 | 43 | 2016 |

Applications of fuzzy rough set theory in machine learning: a survey S Vluymans, L D’eer, Y Saeys, C Cornelis Fundamenta Informaticae 142 (1-4), 53-86, 2015 | 39 | 2015 |

Evolutionary undersampling for imbalanced big data classification I Triguero, M Galar, S Vluymans, C Cornelis, H Bustince, F Herrera, ... 2015 IEEE Congress on Evolutionary Computation (CEC), 715-722, 2015 | 34 | 2015 |

Multi-label classification using a fuzzy rough neighborhood consensus S Vluymans, C Cornelis, F Herrera, Y Saeys Information Sciences 433, 96-114, 2018 | 28 | 2018 |

Dynamic affinity-based classification of multi-class imbalanced data with one-versus-one decomposition: a fuzzy rough set approach S Vluymans, A Fernández, Y Saeys, C Cornelis, F Herrera Knowledge and Information Systems 56 (1), 55-84, 2018 | 26 | 2018 |

Learning from imbalanced data S Vluymans Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using …, 2019 | 15 | 2019 |

EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data S Vluymans, I Triguero, C Cornelis, Y Saeys Neurocomputing 216, 596-610, 2016 | 15 | 2016 |

Improving nearest neighbor classification using ensembles of evolutionary generated prototype subsets N Verbiest, S Vluymans, C Cornelis, N García-Pedrajas, Y Saeys Applied Soft Computing 44, 75-88, 2016 | 11 | 2016 |

Fuzzy multi-instance classifiers S Vluymans, DS Tarragó, Y Saeys, C Cornelis, F Herrera IEEE Transactions on Fuzzy Systems 24 (6), 1395-1409, 2016 | 11 | 2016 |

Semi-supervised fuzzy-rough feature selection R Jensen, S Vluymans, N Mac Parthaláin, C Cornelis, Y Saeys Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, 185-195, 2015 | 10 | 2015 |

Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods S Vluymans Springer International Publishing, 2019 | 9 | 2019 |

Weight selection strategies for ordered weighted average based fuzzy rough sets S Vluymans, N Mac Parthaláin, C Cornelis, Y Saeys Information Sciences 501, 155-171, 2019 | 7 | 2019 |

Fuzzy rough sets for self-labelling: An exploratory analysis S Vluymans, N Mac Parthaláin, C Cornelis, Y Saeys 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 931-938, 2016 | 5 | 2016 |

Distributed fuzzy rough prototype selection for big data regression S Vluymans, H Asfoor, Y Saeys, C Cornelis, M Tolentino, A Teredesai, ... 2015 Annual Conference of the North American Fuzzy Information Processing …, 2015 | 5 | 2015 |

Instance selection for imbalanced data S Vluymans, N Verbiest, C Cornelis, Y Saeys WorkshopRough Sets: Theory and Applications (RST&A); held at the 2014 Joint …, 2014 | 5 | 2014 |

Multiple Instance Multiple Label Learning F Herrera, S Ventura, R Bello, C Cornelis, A Zafra, D Sánchez-Tarragó, ... Multiple Instance Learning, 209-230, 2016 | 4 | 2016 |

Multi-instance regression F Herrera, S Ventura, R Bello, C Cornelis, A Zafra, D Sánchez-Tarragó, ... Multiple Instance Learning, 127-140, 2016 | 4 | 2016 |

Unsupervised multiple instance learning F Herrera, S Ventura, R Bello, C Cornelis, A Zafra, D Sánchez-Tarragó, ... Multiple Instance Learning, 141-167, 2016 | 2 | 2016 |