22 citations · 54 across the 8 of their papers we have counts for
10 papers · 1 filter
AdaCC: Cumulative Cost-Sensitive Boosting for Imbalanced Classification
Vasileios Iosifidis, Symeon Papadopoulos, Bodo Rosenhahn +1
Class imbalance poses a major challenge for machine learning as most supervised learning models might exhibit bias towards the majority class and under-perform in the minority clas…
Parity-based Cumulative Fairness-aware Boosting
Vasileios Iosifidis, Arjun Roy, Eirini Ntoutsi
Data-driven AI systems can lead to discrimination on the basis of protected attributes like gender or race. One reason for this behavior is the encoded societal biases in the train…
Online Fairness-Aware Learning with Imbalanced Data Streams
Vasileios Iosifidis, Wenbin Zhang, Eirini Ntoutsi
Data-driven learning algorithms are employed in many online applications, in which data become available over time, like network monitoring, stock price prediction, job application…
Fair-Capacitated Clustering
Tai Le Quy, Arjun Roy, Gunnar Friege +1
Traditionally, clustering algorithms focus on partitioning the data into groups of similar instances. The similarity objective, however, is not sufficient in applications where a f…
Consequence-aware Sequential Counterfactual Generation
Philip Naumann, Eirini Ntoutsi
Counterfactuals have become a popular technique nowadays for interacting with black-box machine learning models and understanding how to change a particular instance to obtain a de…
Drift-Aware Multi-Memory Model for Imbalanced Data Streams
Amir Abolfazli, Eirini Ntoutsi
Online class imbalance learning deals with data streams that are affected by both concept drift and class imbalance. Online learning tries to find a trade-off between exploiting pr…