A. Campagner

发表

Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests

F. Cabitza, C. Di Resta, G. Banfi, 2020, Clinical chemistry and laboratory medicine.

Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: a Feasibility Study

G. Banfi, D. Brinati, A. Campagner, 2020, medRxiv.

Uncertainty representation in dynamical systems using rough set theory

D. Ciucci, A. Campagner, Valentina Dorigatti, 2021, Theor. Comput. Sci..

Decisions are not all equal - Introducing a utility metric based on case-wise raters' perceptions

F. Cabitza, A. Campagner, Federico Sternini, 2022, Comput. Methods Programs Biomed..

A Distributional Approach for Soft Clustering Comparison and Evaluation

T. Denoeux, D. Ciucci, A. Campagner, 2022, ArXiv.

Rough set-based feature selection for weakly labeled data

Eyke Hüllermeier, Davide Ciucci, Andrea Campagner, 2021, Int. J. Approx. Reason..

The importance of being external. methodological insights for the external validation of machine learning models in medicine

F. Cabitza, A. Campagner, A. Carobene, 2021, Comput. Methods Programs Biomed..

Aggregation models in ensemble learning: A large-scale comparison

F. Cabitza, D. Ciucci, A. Campagner, 2022, Inf. Fusion.

The multicenter European Biological Variation Study (EuBIVAS): a new glance provided by the Principal Component Analysis (PCA), a machine learning unsupervised algorithms, based on the basic metabolic panel linked measurands

F. Cabitza, G. Banfi, M. Vidali, 2021, Clinical chemistry and laboratory medicine.

The unbearable (technical) unreliability of automated facial emotion recognition

F. Cabitza, A. Campagner, Martina Mattioli, 2022, Big Data & Society.

Bridging the “last mile” gap between AI implementation and operation: “data awareness” that matters

Federico Cabitza, Andrea Campagner, Clara Balsano, 2020, Annals of translational medicine.

Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition

F. Cabitza, M. Barandas, N. Bento, 2022, Sensors.

Who wants accurate models? Arguing for a different metrics to take classification models seriously pdf

Federico Cabitza, Andrea Campagner, F. Cabitza, 2019, ArXiv.

Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study

F. Cabitza, G. Banfi, A. Campagner, 2020, Journal of Medical Systems.

Three-Way Decision for Handling Uncertainty in Machine Learning: A Narrative Review

Federico Cabitza, Davide Ciucci, Andrea Campagner, 2020, IJCRS.

Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests

F. Cabitza, C. Di Resta, G. Banfi, 2020, medRxiv.

Assessment and prediction of spine surgery invasiveness with machine learning techniques

Federico Cabitza, Andrea Campagner, Pedro Berjano, 2020, Comput. Biol. Medicine.

The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical AI studies

Federico Cabitza, Andrea Campagner, F. Cabitza, 2021, Int. J. Medical Informatics.

As if sand were stone. New concepts and metrics to probe the ground on which to build trustable AI

Federico Cabitza, Andrea Campagner, Luca Maria Sconfienza, 2020, BMC Medical Informatics and Decision Making.

The Elephant in the Machine: Proposing a New Metric of Data Reliability and its Application to a Medical Case to Assess Classification Reliability

Federico Cabitza, Domenico Albano, Davide Orlandi, 2020, Applied Sciences.

Ground truthing from multi-rater labeling with three-way decision and possibility theory

Federico Cabitza, Davide Ciucci, Carl-Magnus Svensson, 2021, Inf. Sci..

The three-way-in and three-way-out framework to treat and exploit ambiguity in data

Federico Cabitza, Davide Ciucci, Andrea Campagner, 2020, Int. J. Approx. Reason..

Aggregation operators on shadowed sets

Yiyu Yao, D. Ciucci, A. Campagner, 2022, Inf. Sci..

Belief functions and rough sets: Survey and new insights

T. Denoeux, D. Ciucci, A. Campagner, 2022, International Journal of Approximate Reasoning.

Unity Is Intelligence: A Collective Intelligence Experiment on ECG Reading to Improve Diagnostic Performance in Cardiology

F. Cabitza, G. Gensini, A. Campagner, 2021, Journal of Intelligence.

A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patients

F. Cabitza, A. Campagner, A. Carobene, 2022, Medical & biological engineering & computing.

Painting the black box white: experimental findings from applying XAI to an ECG reading setting

F. Cabitza, M. Cameli, A. Campagner, 2022, Mach. Learn. Knowl. Extr..

The need to move away from agential-AI: Empirical investigations, useful concepts and open issues

Carla Simone, Federico Cabitza, Andrea Campagner, 2021, Int. J. Hum. Comput. Stud..

Orthopartitions and soft clustering: Soft mutual information measures for clustering validation

Davide Ciucci, Andrea Campagner, D. Ciucci, 2019, Knowl. Based Syst..

Three-Way Classification: Ambiguity and Abstention in Machine Learning

Federico Cabitza, Davide Ciucci, Andrea Campagner, 2019, IJCRS.

Approximate Reaction Systems Based on Rough Set Theory

Davide Ciucci, Andrea Campagner, Valentina Dorigatti, 2020, IJCRS.

A Formal Learning Theory for Three-Way Clustering

D. Ciucci, A. Campagner, Andrea Campagner, 2020, SUM.

Rough-set Based Genetic Algorithms for Weakly Supervised Feature Selection

D. Ciucci, A. Campagner, 2022, IPMU.

AI Shall Have No Dominion: on How to Measure Technology Dominance in AI-supported Human decision-making

F. Cabitza, C. Reverberi, A. Campagner, 2023, CHI.

Rams, hounds and white boxes: Investigating human-AI collaboration protocols in medical diagnosis

F. Cabitza, M. Cameli, M. Barandas, 2023, Artif. Intell. Medicine.

Color Shadows (Part I): Exploratory Usability Evaluation of Activation Maps in Radiological Machine Learning

F. Cabitza, A. Campagner, E. Gallazzi, 2022, International Cross-Domain Conference on Machine Learning and Knowledge Extraction.

Studying human-AI collaboration protocols: the case of the Kasparov’s law in radiological double reading

F. Cabitza, L. Sconfienza, A. Campagner, 2021, Health Inf. Sci. Syst..

H-Accuracy, an Alternative Metric to Assess Classification Models in Medicine

Federico Cabitza, Andrea Campagner, Luca Sconfienza, 2020, MIE.

Assessment of Fast-Track Pathway in Hip and Knee Replacement Surgery by Propensity Score Matching on Patient-Reported Outcomes

F. Cabitza, G. Banfi, A. Campagner, 2023, Diagnostics.

Global Interpretable Calibration Index, a New Metric to Estimate Machine Learning Models' Calibration

F. Cabitza, A. Campagner, Lorenzo Famiglini, 2022, CD-MAKE.

ALL YOU NEED IS HIGHER ACCURACY? ON THE QUEST FOR MINIMUM ACCEPTABLE ACCURACY FOR MEDICAL ARTIFICIAL INTELLIGENCE

F. Cabitza, A. Campagner, A. Ravizza, 2020, Proceedings of the 12th International Conference on e-Health (EH2020).

Quod erat demonstrandum? - Towards a typology of the concept of explanation for the design of explainable AI

F. Cabitza, David Schneeberger, Andreas Holzinger, 2022, Expert Syst. Appl..

Interpretable heartbeat classification using local model-agnostic explanations on ECGs

Federico Cabitza, Hugo Gamboa, Duarte Folgado, 2021, Comput. Biol. Medicine.

Back to the Feature: A Neural-Symbolic Perspective on Explainable AI

Federico Cabitza, Andrea Campagner, F. Cabitza, 2020, CD-MAKE.

Toward a Perspectivist Turn in Ground Truthing for Predictive Computing pdf

Federico Cabitza, Valerio Basile, Michael Fell, 2021, ArXiv.

Ordinal labels in machine learning: a user-centered approach to improve data validity in medical settings

Federico Cabitza, Davide Ciucci, Andrea Seveso, 2020, BMC Medical Informatics and Decision Making.

Evidence of significant difference in key COVID-19 biomarkers during the Italian lockdown strategy. A retrospective study on patients admitted to a hospital emergency department in Northern Italy

F. Cabitza, C. Di Resta, A. Campagner, 2020, Acta bio-medica : Atenei Parmensis.

External validation of Machine Learning models for COVID-19 detection based on Complete Blood Count

F. Cabitza, A. Campagner, A. Carobene, 2021, Health Inf. Sci. Syst..

Everything is varied: The surprising impact of instantial variation on ML reliability

F. Cabitza, A. Campagner, A. Carobene, 2023, Applied Soft Computing.

Toward a Perspectivist Turn in Ground Truthing for Predictive Computing

F. Cabitza, Valerio Basile, A. Campagner, 2021, AAAI.

Three-Way and Semi-supervised Decision Tree Learning Based on Orthopartitions

Davide Ciucci, Andrea Campagner, D. Ciucci, 2018, IPMU.

Scikit-Weak: A Python Library for Weakly Supervised Machine Learning

E. Hüllermeier, D. Ciucci, A. Campagner, 2022, IJCRS.

Learning from fuzzy labels: Theoretical issues and algorithmic solutions

A. Campagner, Andrea Campagner, 2023, International Journal of Approximate Reasoning.

Three-way decision and conformal prediction: Isomorphisms, differences and theoretical properties of cautious learning approaches

Federico Cabitza, Davide Ciucci, Pedro Berjano, 2021, Inf. Sci..

Learnability in “Learning from Fuzzy Labels”

A. Campagner, 2021, 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

Feature Reduction in Superset Learning Using Rough Sets and Evidence Theory

Eyke Hüllermeier, Davide Ciucci, Andrea Campagner, 2020, IPMU.

Feature Selection and Disambiguation in Learning from Fuzzy Labels Using Rough Sets

Davide Ciucci, Andrea Campagner, D. Ciucci, 2021, IJCRS.

Exploring Medical Data Classification with Three-Way Decision Trees

Federico Cabitza, Davide Ciucci, Andrea Campagner, 2019, HEALTHINF.

The IJMEDI checklist for assessment of medical AI

F. Cabitza, A. Campagner, 2021 .

A Question of Trust: Old and New Metrics for the Reliable Assessment of Trustworthy AI

F. Cabitza, A. Campagner, Riccardo Angius, 2023, HEALTHINF.

Entropy‐based shadowed set approximation of intuitionistic fuzzy sets

Davide Ciucci, Andrea Campagner, Valentina Dorigatti, 2020, Int. J. Intell. Syst..