89 citations · 92 across the 7 of their papers we have counts for
7 papers
Multi-Label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samples
Ao Zhou, Bin Liu, Jin Wang +1
Deep neural network models have demonstrated their effectiveness in classifying multi-label data from various domains. Typically, they employ a training mode that combines mini-bat…
Local Interpretability of Random Forests for Multi-Target Regression
Avraam Bardos, Nikolaos Mylonas, Ioannis Mollas +1
Multi-target regression is useful in a plethora of applications. Although random forest models perform well in these tasks, they are often difficult to interpret. Interpretability…
Does Noise Affect Housing Prices? A Case Study in the Urban Area of Thessaloniki
Georgios Kamtziridis, Dimitris Vrakas, Grigorios Tsoumakas
Real estate markets depend on various methods to predict housing prices, including models that have been trained on datasets of residential or commercial properties. Most studies e…
An Attention Matrix for Every Decision: Faithfulness-based Arbitration Among Multiple Attention-Based Interpretations of Transformers in Text Classification
Nikolaos Mylonas, Ioannis Mollas, Grigorios Tsoumakas
Transformers are widely used in natural language processing, where they consistently achieve state-of-the-art performance. This is mainly due to their attention-based architecture,…
Hierarchical Partitioning of the Output Space in Multi-label Data
Yannis Papanikolaou, Ioannis Katakis, Grigorios Tsoumakas
Hierarchy Of Multi-label classifiers (HOMER) is a multi-label learning algorithm that breaks the initial learning task to several, easier sub-tasks by first constructing a hierarch…
Multi-Target Regression via Random Linear Target Combinations
Grigorios Tsoumakas, Eleftherios Spyromitros-Xioufis, Aikaterini Vrekou +1
Multi-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables. It arises in several interes…