119 citations · 157 across the 11 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
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…
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…