26 citations · 71 across the 11 of their papers we have counts for
12 papers
Classifying token frequencies using angular Minkowski -distance
Oliver Urs Lenz, Chris Cornelis
Angular Minkowski -distance is a dissimilarity measure that is obtained by replacing Euclidean distance in the definition of cosine dissimilarity with other Minkowski -distan…
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…
A Novel Machine Learning Approach to Data Inconsistency with respect to a Fuzzy Relation
Marko Palangetić, Chris Cornelis, Salvatore Greco +1
Inconsistency in prediction problems occurs when instances that relate in a certain way on condition attributes, do not follow the same relation on the decision attribute. For exam…
Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets
Olha Kaminska, Chris Cornelis, Veronique Hoste
Social media are an essential source of meaningful data that can be used in different tasks such as sentiment analysis and emotion recognition. Mostly, these tasks are solved with…
Optimised one-class classification performance
Oliver Urs Lenz, Daniel Peralta, Chris Cornelis
We provide a thorough treatment of one-class classification with hyperparameter optimisation for five data descriptors: Support Vector Machine (SVM), Nearest Neighbour Distance (NN…
Average Localised Proximity: A new data descriptor with good default one-class classification performance
Oliver Urs Lenz, Daniel Peralta, Chris Cornelis
One-class classification is a challenging subfield of machine learning in which so-called data descriptors are used to predict membership of a class based solely on positive exampl…