26 citations · 71 across the 11 of their papers we have counts for
7 papers · 1 filter
Fuzzy Rough Sets Based on Fuzzy Quantification
Adnan Theerens, Chris Cornelis
One of the weaknesses of classical (fuzzy) rough sets is their sensitivity to noise, which is particularly undesirable for machine learning applications. One approach to solve this…
Evaluation of the impact of the indiscernibility relation on the fuzzy-rough nearest neighbours algorithm
Henri Bollaert, Chris Cornelis
Fuzzy rough sets are well-suited for working with vague, imprecise or uncertain information and have been succesfully applied in real-world classification problems. One of the prom…
Polar Encoding: A Simple Baseline Approach for Classification with Missing Values
Oliver Urs Lenz, Daniel Peralta, Chris Cornelis
We propose polar encoding, a representation of categorical and numerical -valued attributes with missing values to be used in a classification context. We argue that this is…
Fuzzy granular approximation classifier
Marko Palangetić, Chris Cornelis, Salvatore Greco +1
In this article, a new Fuzzy Granular Approximation Classifier (FGAC) is introduced. The classifier is based on the previously introduced concept of the granular approximation and…
No imputation without representation
Oliver Urs Lenz, Daniel Peralta, Chris Cornelis
By filling in missing values in datasets, imputation allows these datasets to be used with algorithms that cannot handle missing values by themselves. However, missing values may i…
Choquet-Based Fuzzy Rough Sets
Adnan Theerens, Oliver Urs Lenz, Chris Cornelis
Fuzzy rough set theory can be used as a tool for dealing with inconsistent data when there is a gradual notion of indiscernibility between objects. It does this by providing lower…
Multi-class granular approximation by means of disjoint and adjacent fuzzy granules
Marko Palangetić, Chris Cornelis, Salvatore Greco +1
In granular computing, fuzzy sets can be approximated by granularly representable sets that are as close as possible to the original fuzzy set w.r.t. a given closeness measure. Suc…