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Found 13 papers, 8 papers with code
Truthful Meta-Explanations for Local Interpretability of Machine Learning Models
As a result, the demand for a selection tool, a meta-explanation technique based on a high-quality evaluation metric, is apparent.
Local Multi-Label Explanations for Random Forest
Random Forest falls short on this property, especially when a large number of tree predictors are used.
Local Explanation of Dimensionality Reduction
Dimensionality reduction (DR) is a popular method for preparing and analyzing high-dimensional data.
Intelligent Momentary Assisted Control for Autonomous Emergency Braking
In this study, we design a safety distance-based hierarchical AEB control system constituted of a high-level Rule-Based Supervisory control module, an intermediate-level switching algorithm and a low-level control module.
LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information
Artificial Intelligence (AI) has a tremendous impact on the unexpected growth of technology in almost every aspect.
Conclusive Local Interpretation Rules for Random Forests
LionForests is a random forest-specific interpretation technique, which provides rules as explanations.
VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance
The use of machine learning rapidly increases in high-risk scenarios where decisions are required, for example in healthcare or industrial monitoring equipment.
Altruist: Argumentative Explanations through Local Interpretations of Predictive Models
Lack of evaluation and selection criteria also makes it difficult for the end user to choose the most suitable technique.
Mechanism Design for Efficient Online and Offline Allocation of Electric Vehicles to Charging Stations
We formulate the problem of the optimal EV to charging station allocation as a Mixed Integer Programming (MIP) one and we propose two pricing mechanisms: A fixed-price one, and another that is based on the well known Vickrey-Clark-Groves (VCG) mechanism.
Computer Science and Game Theory Systems and Control Systems and Control
A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning
Object perception is a fundamental sub-field of Computer Vision, covering a multitude of individual areas and having contributed high-impact results.
LionForests: Local Interpretation of Random Forests
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 exclusively on enhancing their performance.
SWRL2SPIN: A tool for transforming SWRL rule bases in OWL ontologies to object-oriented SPIN rules
Semantic Web Rule Language (SWRL) combines OWL (Web Ontology Language) ontologies with Horn Logic rules of the Rule Markup Language (RuleML) family.
The Tomaco Hybrid Matching Framework for SAWSDL Semantic Web Services
The algorithm is hybrid in nature, combining novel and known concepts, such as a logic-based strategy and syntactic text-similarity measures on semantic annotations and textual descriptions.
