Search Results for author:
Found 31 papers, 5 papers with code
MDF-Net for abnormality detection by fusing X-rays with clinical data
In this work, we propose a novel architecture consisting of two fusion methods that enable the model to simultaneously process patients' clinical data (structured data) and chest X-rays (image data).
Benchmarking Counterfactual Algorithms for XAI: From White Box to Black Box
This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of models: decision-tree (fully transparent, interpretable, white-box model), a random forest (a semi-interpretable, grey-box model), and a neural network (a fully opaque, black-box model).
Evaluating Explainable Methods for Predictive Process Analytics: A Functionally-Grounded Approach
Current explainable machine learning methods, such as LIME and SHAP, can be used to interpret black box models.
Developing a Fidelity Evaluation Approach for Interpretable Machine Learning
Although modern machine learning and deep learning methods allow for complex and in-depth data analytics, the predictive models generated by these methods are often highly complex, and lack transparency.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
+2
The Dutch's Real World Financial Institute: Introducing Quantum-Like Bayesian Networks as an Alternative Model to deal with Uncertainty
In this work, we analyse and model a real life financial loan application belonging to a sample bank in the Netherlands.
The Relation Between Acausality and Interference in Quantum-Like Bayesian Networks
We analyse a quantum-like Bayesian Network that puts together cause/effect relationships and semantic similarities between events.
An Experiment on Using Bayesian Networks for Process Mining
Process mining is a technique that performs an automatic analysis of business processes from a log of events with the promise of understanding how processes are executed in an organisation.
Interference Effects in Quantum Belief Networks
This means that probabilistic graphical models based on classical probability theory are too limited to fully simulate and explain various aspects of human decision making.
Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy
To deal with these conflicts, we applied the Dempster-Shafer theory of evidence combined with Shannon's Entropy formula to fuse this information and come up with a more accurate and reliable final ranking list.
Introducing Quantum-Like Influence Diagrams for Violations of the Sure Thing Principle
The general idea is to take advantage of the quantum interference terms produced in the quantum-like Bayesian Network to influence the probabilities used to compute the expected utility of some action.
Unifying Decision-Making: a Review on Evolutionary Theories on Rationality and Cognitive Biases
In this paper, we make a review on the concepts of rationality across several different fields, namely in economics, psychology and evolutionary biology and behavioural ecology.
Towards a Quantum-Like Cognitive Architecture for Decision-Making
We propose an alternative and unifying framework for decision-making that, by using quantum mechanics, provides more generalised cognitive and decision models with the ability to represent more information than classical models.
Exploring Interpretability for Predictive Process Analytics
The explanations allow us to gain an understanding of the underlying reasons for a prediction and highlight scenarios where accuracy alone may not be sufficient in assessing the suitability of techniques used to encode event log data to features used by a predictive model.
An Investigation of Interpretability Techniques for Deep Learning in Predictive Process Analytics
We see certain distinct features used for predictions that provide useful insights about the type of cancer, along with features that do not generalize well.
Decision Making
Interpretability Techniques for Deep Learning
QuLBIT: Quantum-Like Bayesian Inference Technologies for Cognition and Decision
This paper provides the foundations of a unified cognitive decision-making framework (QulBIT) which is derived from quantum theory.
Construction of 'Support Vector' Machine Feature Spaces via Deformed Weyl-Heisenberg Algebra
This paper uses deformed coherent states, based on a deformed Weyl-Heisenberg algebra that unifies the well-known SU(2), Weyl-Heisenberg, and SU(1, 1) groups, through a common parameter.
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models
This has led to an increased interest in interpretable machine learning, where post hoc interpretation presents a useful mechanism for generating interpretations of complex learning models.
On Projection Based Operators in Lp space for Exact Similarity Search
We investigate exact indexing for high dimensional Lp norms based on the 1-Lipschitz property and projection operators.
Using Rank Aggregation for Expert Search in Academic Digital Libraries
The task of expert finding has been getting increasing attention in information retrieval literature.
Learning to Rank for Expert Search in Digital Libraries of Academic Publications
The task of expert finding has been getting increasing attention in information retrieval literature.
Learning to Rank Academic Experts in the DBLP Dataset
More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combing all of the estimators of expertise.
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications
This paper presents an in-depth systematic review of the diverse existing body of literature on counterfactuals and causability for explainable artificial intelligence.
Order Effects in Bayesian Updates
Order effects occur when judgments about a hypothesis's probability given a sequence of information do not equal the probability of the same hypothesis when the information is reversed.
DiCE4EL: Interpreting Process Predictions using a Milestone-Aware Counterfactual Approach
We design an extension of DiCE, namely DiCE4EL (DiCE for Event Logs), that can generate counterfactual explanations for process prediction, and propose an approach that supports deriving milestone-aware counterfactual explanations at key intermediate stages along process execution to promote interpretability.
Explainable AI Enabled Inspection of Business Process Prediction Models
As an important branch of state-of-the-art data analytics, business process predictions are also faced with a challenge in regard to the lack of explanation to the reasoning and decision by the underlying `black-box' prediction models.
Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms
In this paper, we address the "black-box" problem in predictive process analytics by building interpretable models that are capable to inform both what and why is a prediction.
An Extension Of Combinatorial Contextuality For Cognitive Protocols
Through this formalisation, we extend the combinatorial approach to support a measurement and treatment of disturbance, and offer techniques to separately distinguish noise and causal influences.
Improving X-ray Diagnostics through Eye-Tracking and XR
There is a growing need to assist radiologists in performing X-ray readings and diagnoses fast, comfortably, and effectively.
Development of an Immersive Virtual Colonoscopy Viewer for Colon Growths Diagnosis
Desktop-based virtual colonoscopy has been proven to be an asset in the identification of colon anomalies.
Integrating Eye-Gaze Data into CXR DL Approaches: A Preliminary study
This paper proposes a novel multimodal DL architecture incorporating medical images and eye-tracking data for abnormality detection in chest x-rays.
Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes
In recent years, various machine and deep learning architectures have been successfully introduced to the field of predictive process analytics.
