19 citations · 67 across the 13 of their papers we have counts for
14 papers
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…
Intelligent Momentary Assisted Control for Autonomous Emergency Braking
Konstantinos Gounis, Nick Bassiliades
Development of control algorithms for enhancing performance in safety-critical systems such as the Autonomous Emergency Braking system (AEB) is an important issue in the emerging f…
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…
Mechanism Design for Efficient Online and Offline Allocation of Electric Vehicles to Charging Stations
Emmanouil Rigas, Enrico Gerding, Sebastian Stein +2
We study the problem of allocating Electric Vehicles (EVs) to charging stations and scheduling their charging. We develop offline and online solutions that treat EV users as self-i…
A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning
Filippos Gouidis, Alexandros Vassiliades, Theodore Patkos +3
Object perception is a fundamental sub-field of Computer Vision, covering a multitude of individual areas and having contributed high-impact results. While Machine Learning has bee…
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…
SWRL2SPIN: A tool for transforming SWRL rule bases in OWL ontologies to object-oriented SPIN rules
Nick Bassiliades
Semantic Web Rule Language (SWRL) combines OWL (Web Ontology Language) ontologies with Horn Logic rules of the Rule Markup Language (RuleML) family. Being supported by ontology edi…
The Tomaco Hybrid Matching Framework for SAWSDL Semantic Web Services
Thanos G. Stavropoulos, Stelios Andreadis, Nick Bassiliades +2
This work aims to resolve issues related to Web Service retrieval, also known as Service Selection, Discovery or essentially Matching, in two directions. Firstly, a novel matching…
DISARM: A Social Distributed Agent Reputation Model based on Defeasible Logic
Kalliopi Kravari, Nick Bassiliades
Intelligent Agents act in open and thus risky environments, hence making the appropriate decision about who to trust in order to interact with, could be a challenging process. As i…