338 citations · 698 across the 54 of their papers we have counts for
23 papers · 1 filter
Addressing Imbalance in Multi-Label Data via Label-Specific Distance-based Oversampling
Bin Liu, Jun Wu, Haoyu Peng +4
The complex imbalanced label distribution poses a crucial challenge to multi-label classification, as most classifiers are biased towards the majority class and high-frequent label…
D2ACE: Multi-Label Batch Selection Guided by Dual Dynamics and Adaptive Correlation Enhancement
Bin Liu, Haoyu Peng, Zhijia Wei +2
Batch selection is crucial for improving both training efficiency and predictive performance in deep multi-label classification (MLC). Existing batch selection methods typically re…
Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label Correlations
Ao Zhou, Bin Liu, Jin Wang +1
The accuracy of deep neural networks is significantly influenced by the effectiveness of mini-batch construction during training. In single-label scenarios, such as binary and mult…
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…
Truthful Meta-Explanations for Local Interpretability of Machine Learning Models
Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas
Automated Machine Learning-based systems' integration into a wide range of tasks has expanded as a result of their performance and speed. Although there are numerous advantages to…
Local Multi-Label Explanations for Random Forest
Nikolaos Mylonas, Ioannis Mollas, Nick Bassiliades +1
Multi-label classification is a challenging task, particularly in domains where the number of labels to be predicted is large. Deep neural networks are often effective at multi-lab…
Local Explanation of Dimensionality Reduction
Avraam Bardos, Ioannis Mollas, Nick Bassiliades +1
Dimensionality reduction (DR) is a popular method for preparing and analyzing high-dimensional data. Reduced data representations are less computationally intensive and easier to m…
Short-term Renewable Energy Forecasting in Greece using Prophet Decomposition and Tree-based Ensembles
Argyrios Vartholomaios, Stamatis Karlos, Eleftherios Kouloumpris +1
Energy production using renewable sources exhibits inherent uncertainties due to their intermittent nature. Nevertheless, the unified European energy market promotes the increasing…
VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance
Spyridon Paraschos, Ioannis Mollas, Nick Bassiliades +1
The use of machine learning rapidly increases in high-risk scenarios where decisions are required, for example in healthcare or industrial monitoring equipment. In crucial situatio…
LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information
Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas
Artificial Intelligence (AI) has a tremendous impact on the unexpected growth of technology in almost every aspect. AI-powered systems are monitoring and deciding about sensitive e…
Conclusive Local Interpretation Rules for Random Forests
Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas
In critical situations involving discrimination, gender inequality, economic damage, and even the possibility of casualties, machine learning models must be able to provide clear i…
Drug-Target Interaction Prediction via an Ensemble of Weighted Nearest Neighbors with Interaction Recovery
Bin Liu, Konstantinos Pliakos, Celine Vens +1
Predicting drug-target interactions (DTI) via reliable computational methods is an effective and efficient way to mitigate the enormous costs and time of the drug discovery process…
Altruist: Argumentative Explanations through Local Interpretations of Predictive Models
Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas
Explainable AI is an emerging field providing solutions for acquiring insights into automated systems' rationale. It has been put on the AI map by suggesting ways to tackle key eth…
Multi-Label Sampling based on Local Label Imbalance
Bin Liu, Konstantinos Blekas, Grigorios Tsoumakas
Class imbalance is an inherent characteristic of multi-label data that hinders most multi-label learning methods. One efficient and flexible strategy to deal with this problem is t…
LionForests: Local Interpretation of Random Forests
Ioannis Mollas, Nick Bassiliades, Ioannis Vlahavas +1
Towards a future where machine learning systems will integrate into every aspect of people's lives, researching methods to interpret such systems is necessary, instead of focusing…
Synthetic Oversampling of Multi-Label Data based on Local Label Distribution
Bin Liu, Grigorios Tsoumakas
Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods. One efficient strategy to deal with th…
LioNets: Local Interpretation of Neural Networks through Penultimate Layer Decoding
Ioannis Mollas, Nikolaos Bassiliades, Grigorios Tsoumakas
Technological breakthroughs on smart homes, self-driving cars, health care and robotic assistants, in addition to reinforced law regulations, have critically influenced academic re…
Making Classifier Chains Resilient to Class Imbalance
Bin Liu, Grigorios Tsoumakas
Class imbalance is an intrinsic characteristic of multi-label data. Most of the labels in multi-label data sets are associated with a small number of training examples, much smalle…
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…
Discovering and Exploiting Entailment Relationships in Multi-Label Learning
Christina Papagiannopoulou, Grigorios Tsoumakas, Ioannis Tsamardinos
This work presents a sound probabilistic method for enforcing adherence of the marginal probabilities of a multi-label model to automatically discovered deterministic relationships…
Multi-Target Regression via Input Space Expansion: Treating Targets as Inputs
Eleftherios Spyromitros-Xioufis, Grigorios Tsoumakas, William Groves +1
In many practical applications of supervised learning the task involves the prediction of multiple target variables from a common set of input variables. When the prediction target…