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ORCIDAndrea Campagner
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2020 – today
- 2024
[j37]Andrea Campagner, Frida Milella, Davide Ciucci, Federico Cabitza:
Three-way decision in machine learning tasks: a systematic review. Artif. Intell. Rev. 57(9): 228 (2024)- [j36]Federico Cabitza
, Chiara Natali, Lorenzo Famiglini, Andrea Campagner, Valerio Caccavella, Enrico Gallazzi:
Never tell me the odds: Investigating pro-hoc explanations in medical decision making. Artif. Intell. Medicine 150: 102819 (2024) - [j35]Lorenzo Famiglini, Andrea Campagner, Marília Barandas, Giovanni Andrea La Maida, Enrico Gallazzi, Federico Cabitza:
Evidence-based XAI: An empirical approach to design more effective and explainable decision support systems. Comput. Biol. Medicine 170: 108042 (2024) - [j34]Andrea Campagner:
Learning from fuzzy labels: Theoretical issues and algorithmic solutions. Int. J. Approx. Reason. 171: 108969 (2024) - [j33]Marília Barandas, Lorenzo Famiglini, Andrea Campagner, Duarte Folgado, Raquel Simão, Federico Cabitza, Hugo Gamboa:
Evaluation of uncertainty quantification methods in multi-label classification: A case study with automatic diagnosis of electrocardiogram. Inf. Fusion 101: 101978 (2024) - [j32]Stefania Boffa, Andrea Campagner, Davide Ciucci:
Partially-defined equivalence relations: Relationship with orthopartitions and connection to rough sets. Inf. Sci. 657: 119941 (2024) - [j31]Andrea Campagner, Frida Milella, Giuseppe Banfi, Federico Cabitza:
Second opinion machine learning for fast-track pathway assignment in hip and knee replacement surgery: the use of patient-reported outcome measures. BMC Medical Informatics Decis. Mak. 24(4): 203 (2024) - [j30]Andrea Campagner, Marília Barandas, Duarte Folgado, Hugo Gamboa, Federico Cabitza:
Ensemble Predictors: Possibilistic Combination of Conformal Predictors for Multivariate Time Series Classification. IEEE Trans. Pattern Anal. Mach. Intell. 46(11): 7205-7216 (2024) - [c45]Chiara Natali, Andrea Campagner, Federico Cabitza:
Answering the Call to Go Beyond Accuracy: An Online Tool for the Multidimensional Assessment of Decision Support Systems. BIOSTEC (2) 2024: 219-229 - [c44]Federico Cabitza, Lorenzo Famiglini, Andrea Campagner, Luca Maria Sconfienza, Stefano Fusco, Valerio Caccavella, Enrico Gallazzi:
Dissimilar Similarities: Comparing Human and Statistical Similarity Evaluation in Medical AI. MDAI 2024: 187-198 - [c43]Federico Cabitza, Caterina Fregosi, Andrea Campagner, Chiara Natali:
Explanations Considered Harmful: The Impact of Misleading Explanations on Accuracy in Hybrid Human-AI Decision Making. xAI (4) 2024: 255-269 - 2023
- [j29]Federico Cabitza, Andrea Campagner, Luca Ronzio, Matteo Cameli, Giulia Elena Mandoli, Maria Concetta Pastore, Luca Maria Sconfienza, Duarte Folgado, Marília Barandas, Hugo Gamboa:
Rams, hounds and white boxes: Investigating human-AI collaboration protocols in medical diagnosis. Artif. Intell. Medicine 138: 102506 (2023) - [j28]Andrea Campagner, Lorenzo Famiglini, Anna Carobene, Federico Cabitza:
Everything is varied: The surprising impact of instantial variation on ML reliability. Appl. Soft Comput. 146: 110644 (2023) - [j27]Federico Cabitza, Andrea Campagner, Gianclaudio Malgieri, Chiara Natali, David Schneeberger, Karl Stöger, Andreas Holzinger:
Quod erat demonstrandum? - Towards a typology of the concept of explanation for the design of explainable AI. Expert Syst. Appl. 213(Part): 118888 (2023) - [j26]Andrea Campagner, Davide Ciucci, Thierry Denoeux:
A distributional framework for evaluation, comparison and uncertainty quantification in soft clustering. Int. J. Approx. Reason. 162: 109008 (2023) - [j25]Andrea Campagner, Davide Ciucci, Federico Cabitza:
Aggregation models in ensemble learning: A large-scale comparison. Inf. Fusion 90: 241-252 (2023) - [j24]Andrea Campagner, Davide Ciucci, Thierry Denoeux:
A general framework for evaluating and comparing soft clusterings. Inf. Sci. 623: 70-93 (2023) - [j23]Federico Cabitza, Andrea Campagner, Chiara Natali, Enea Parimbelli, Luca Ronzio, Matteo Cameli:
Painting the Black Box White: Experimental Findings from Applying XAI to an ECG Reading Setting. Mach. Learn. Knowl. Extr. 5(1): 269-286 (2023) - [c42]Federico Cabitza, Andrea Campagner, Valerio Basile:
Toward a Perspectivist Turn in Ground Truthing for Predictive Computing. AAAI 2023: 6860-6868 - [c41]Andrea Campagner, Lorenzo Famiglini, Beatrice Arosio, Paolo Rossi, Giorgio Annoni, Federico Cabitza:
Biomarkers for mixed dementia: a hard bone to bite? Preliminary analyses and promising results for a debated topic. AIxAS@AI*IA 2023: 136-143 - [c40]Andrea Campagner, Riccardo Angius, Federico Cabitza:
A Question of Trust: Old and New Metrics for the Reliable Assessment of Trustworthy AI. HEALTHINF 2023: 132-143 - [c39]Peter Kieseberg, Edgar R. Weippl, A Min Tjoa, Federico Cabitza, Andrea Campagner, Andreas Holzinger:
Controllable AI - An Alternative to Trustworthiness in Complex AI Systems? CD-MAKE 2023: 1-12 - [c38]David Schneeberger, Richard Röttger, Federico Cabitza, Andrea Campagner, Markus Plass, Heimo Müller, Andreas Holzinger:
The Tower of Babel in Explainable Artificial Intelligence (XAI). CD-MAKE 2023: 65-81 - [c37]Federico Cabitza, Andrea Campagner, Lorenzo Famiglini, Chiara Natali, Valerio Caccavella, Enrico Gallazzi:
Let Me Think! Investigating the Effect of Explanations Feeding Doubts About the AI Advice. CD-MAKE 2023: 155-169 - [c36]Federico Cabitza, Andrea Campagner, Riccardo Angius, Chiara Natali, Carlo Reverberi:
AI Shall Have No Dominion: on How to Measure Technology Dominance in AI-supported Human decision-making. CHI 2023: 354:1-354:20 - [c35]Federico Cabitza, Andrea Campagner, Chiara Natali:
Demo: Decision Support System Quality Assessment Tool. CHItaly 2023: 57:1-57:4 - [c34]Andrea Campagner:
Credal Learning: Weakly Supervised Learning from Credal Sets. ECAI 2023: 327-334 - [c33]Lorenzo Famiglini, Andrea Campagner, Federico Cabitza:
Towards a Rigorous Calibration Assessment Framework: Advancements in Metrics, Methods, and Use. ECAI 2023: 645-652 - [c32]Frida Milella, Chiara Natali, Teresa Scantamburlo, Andrea Campagner, Federico Cabitza:
The Impact of Gender and Personality in Human-AI Teaming: The Case of Collaborative Question Answering. INTERACT (2) 2023: 329-349 - [c31]Stefania Boffa, Andrea Campagner, Davide Ciucci, Yiyu Yao:
Aggregation Operators on Shadowed Sets Deriving from Conditional Events and Consensus Operators. IJCRS 2023: 201-215 - [c30]Chiara Natali, Lorenzo Famiglini, Andrea Campagner, Giovanni Andrea La Maida, Enrico Gallazzi, Federico Cabitza:
Color Shadows 2: Assessing the Impact of XAI on Diagnostic Decision-Making. xAI (1) 2023: 618-629 - [e2]Andreas Holzinger, Peter Kieseberg, Federico Cabitza, Andrea Campagner, A Min Tjoa, Edgar R. Weippl:
Machine Learning and Knowledge Extraction - 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Benevento, Italy, August 29 - September 1, 2023, Proceedings. Lecture Notes in Computer Science 14065, Springer 2023, ISBN 978-3-031-40836-6 [contents] - [e1]Andrea Campagner, Oliver Urs Lenz, Shuyin Xia, Dominik Slezak, Jaroslaw Was, JingTao Yao:
Rough Sets - International Joint Conference, IJCRS 2023, Krakow, Poland, October 5-8, 2023, Proceedings. Lecture Notes in Computer Science 14481, Springer 2023, ISBN 978-3-031-50958-2 [contents] - 2022
- [j22]Federico Cabitza, Andrea Campagner, Martina Mattioli:
The unbearable (technical) unreliability of automated facial emotion recognition. Big Data Soc. 9(2): 205395172211295 (2022) - [j21]Andrea Campagner, Federico Sternini, Federico Cabitza:
Decisions are not all equal - Introducing a utility metric based on case-wise raters' perceptions. Comput. Methods Programs Biomed. 221: 106930 (2022) - [j20]Andrea Campagner, Davide Ciucci, Thierry Denoeux:
Belief functions and rough sets: Survey and new insights. Int. J. Approx. Reason. 143: 192-215 (2022) - [j19]Stefania Boffa, Andrea Campagner, Davide Ciucci, Yiyu Yao:
Aggregation operators on shadowed sets. Inf. Sci. 595: 313-333 (2022) - [j18]Nuno Bento, Joana Rebelo, Marília Barandas, André V. Carreiro, Andrea Campagner, Federico Cabitza, Hugo Gamboa:
Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition. Sensors 22(19): 7324 (2022) - [j17]Andrea Campagner, Davide Ciucci, Valentina Dorigatti:
Uncertainty representation in dynamical systems using rough set theory. Theor. Comput. Sci. 908: 28-42 (2022) - [c29]Andrea Campagner, Davide Ciucci, Thierry Denoeux:
A Distributional Approach for Soft Clustering Comparison and Evaluation. BELIEF 2022: 3-12 - [c28]Federico Cabitza, Andrea Campagner, Lorenzo Famiglini, Enrico Gallazzi, Giovanni Andrea La Maida:
Color Shadows (Part I): Exploratory Usability Evaluation of Activation Maps in Radiological Machine Learning. CD-MAKE 2022: 31-50 - [c27]Federico Cabitza, Andrea Campagner, Lorenzo Famiglini:
Global Interpretable Calibration Index, a New Metric to Estimate Machine Learning Models' Calibration. CD-MAKE 2022: 82-99 - [c26]Andrea Campagner, Davide Ciucci:
Three-way Learnability: A Learning Theoretic Perspective on Three-way Decision. FedCSIS 2022: 243-246 - [c25]Andrea Campagner, Davide Ciucci:
Rough-set Based Genetic Algorithms for Weakly Supervised Feature Selection. IPMU (2) 2022: 761-773 - [c24]Federico Cabitza, Andrea Campagner, Enrico Conte:
Comparative Assessment of Two Data Visualizations to Communicate Medical Test Results Online. VISIGRAPP (3: IVAPP) 2022: 195-202 - [c23]Andrea Campagner, Lorenzo Famiglini, Federico Cabitza:
Re-calibrating Machine Learning Models Using Confidence Interval Bounds. MDAI 2022: 132-142 - [c22]Andrea Campagner, Lorenzo Famiglini, Federico Cabitza:
A Confidence Interval-Based Method for Classifier Re-Calibration. MIE 2022: 127-128 - [c21]Davide Ciucci, Stefania Boffa, Andrea Campagner:
Orthopartitions in Knowledge Representation and Machine Learning. IJCRS 2022: 3-18 - [c20]Andrea Campagner, Julian Lienen, Eyke Hüllermeier, Davide Ciucci:
Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. IJCRS 2022: 57-70 - [i5]Andrea Campagner, Davide Ciucci, Thierry Denoeux:
A Distributional Approach for Soft Clustering Comparison and Evaluation. CoRR abs/2206.09827 (2022) - [i4]Andrea Campagner, Lorenzo Famiglini, Anna Carobene, Federico Cabitza:
Everything is Varied: The Surprising Impact of Individual Variation on ML Robustness in Medicine. CoRR abs/2210.04555 (2022) - [i3]Federico Cabitza, Matteo Cameli, Andrea Campagner, Chiara Natali, Luca Ronzio:
Painting the black box white: experimental findings from applying XAI to an ECG reading setting. CoRR abs/2210.15236 (2022) - 2021
- [j16]Inês Neves, Duarte Folgado, Sara Santos, Marília Barandas, Andrea Campagner, Luca Ronzio, Federico Cabitza, Hugo Gamboa:
Interpretable heartbeat classification using local model-agnostic explanations on ECGs. Comput. Biol. Medicine 133: 104393 (2021) - [j15]Federico Cabitza, Andrea Campagner, Felipe Soares, Luis García de Guadiana-Romualdo, Feyissa Challa, Adela Sulejmani, Michela Seghezzi, Anna Carobene:
The importance of being external. methodological insights for the external validation of machine learning models in medicine. Comput. Methods Programs Biomed. 208: 106288 (2021) - [j14]Federico Cabitza, Andrea Campagner, Luca Maria Sconfienza:
Studying human-AI collaboration protocols: the case of the Kasparov's law in radiological double reading. Health Inf. Sci. Syst. 9(1): 8 (2021) - [j13]Andrea Campagner, Anna Carobene, Federico Cabitza:
External validation of Machine Learning models for COVID-19 detection based on Complete Blood Count. Health Inf. Sci. Syst. 9(1): 37 (2021) - [j12]Andrea Campagner, Davide Ciucci, Eyke Hüllermeier:
Rough set-based feature selection for weakly labeled data. Int. J. Approx. Reason. 136: 150-167 (2021) - [j11]Federico Cabitza, Andrea Campagner:
The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical AI studies. Int. J. Medical Informatics 153: 104510 (2021) - [j10]Federico Cabitza, Andrea Campagner, Carla Simone:
The need to move away from agential-AI: Empirical investigations, useful concepts and open issues. Int. J. Hum. Comput. Stud. 155: 102696 (2021) - [j9]Andrea Campagner, Davide Ciucci, Carl-Magnus Svensson, Marc Thilo Figge, Federico Cabitza:
Ground truthing from multi-rater labeling with three-way decision and possibility theory. Inf. Sci. 545: 771-790 (2021) - [j8]Andrea Campagner, Federico Cabitza, Pedro Berjano, Davide Ciucci:
Three-way decision and conformal prediction: Isomorphisms, differences and theoretical properties of cautious learning approaches. Inf. Sci. 579: 347-367 (2021) - [c19]Lorenzo Famiglini, Giorgio Bini, Anna Carobene, Andrea Campagner, Federico Cabitza:
Prediction of ICU admission for COVID-19 patients: a Machine Learning approach based on Complete Blood Count data. CBMS 2021: 160-165 - [c18]Andrea Campagner, Enrico Conte, Federico Cabitza:
Weighted Utility: A Utility Metric Based on the Case-Wise Raters' Perceptions. CD-MAKE 2021: 203-210 - [c17]Andrea Campagner:
Learnability in "Learning from Fuzzy Labels". FUZZ-IEEE 2021: 1-6 - [c16]Federico Cabitza, Andrea Campagner, Valentina Cavosi:
Assessing the impact of medical AI: a survey of physicians' perceptions. ICMHI 2021: 225-231 - [c15]Andrea Campagner, Davide Ciucci:
Feature Selection and Disambiguation in Learning from Fuzzy Labels Using Rough Sets. IJCRS 2021: 164-179 - [i2]Valerio Basile, Federico Cabitza, Andrea Campagner, Michael Fell:
Toward a Perspectivist Turn in Ground Truthing for Predictive Computing. CoRR abs/2109.04270 (2021) - 2020
- [j7]Andrea Campagner, Pedro Berjano, Claudio Lamartina, Francesco Langella, Giovanni Lombardi, Federico Cabitza:
Assessment and prediction of spine surgery invasiveness with machine learning techniques. Comput. Biol. Medicine 121: 103796 (2020) - [j6]Andrea Campagner, Federico Cabitza, Davide Ciucci:
The three-way-in and three-way-out framework to treat and exploit ambiguity in data. Int. J. Approx. Reason. 119: 292-312 (2020) - [j5]Andrea Campagner, Valentina Dorigatti, Davide Ciucci:
Entropy-based shadowed set approximation of intuitionistic fuzzy sets. Int. J. Intell. Syst. 35(12): 2117-2139 (2020) - [j4]Davide Brinati, Andrea Campagner, Davide Ferrari, Massimo Locatelli, Giuseppe Banfi, Federico Cabitza:
Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study. J. Medical Syst. 44(8): 135 (2020) - [j3]Federico Cabitza, Andrea Campagner, Luca Maria Sconfienza:
As if sand were stone. New concepts and metrics to probe the ground on which to build trustable AI. BMC Medical Informatics Decis. Mak. 20(1): 219 (2020) - [j2]Andrea Seveso, Andrea Campagner, Davide Ciucci, Federico Cabitza:
Ordinal labels in machine learning: a user-centered approach to improve data validity in medical settings. BMC Medical Informatics Decis. Mak. 20-S(5): 142 (2020) - [c14]Andrea Campagner, Federico Cabitza:
Back to the Feature: A Neural-Symbolic Perspective on Explainable AI. CD-MAKE 2020: 39-55 - [c13]Andrea Campagner, Davide Ciucci, Eyke Hüllermeier:
Feature Reduction in Superset Learning Using Rough Sets and Evidence Theory. IPMU (1) 2020: 471-484 - [c12]Andrea Campagner, Davide Ciucci, Federico Cabitza:
Ensemble Learning, Social Choice and Collective Intelligence - An Experimental Comparison of Aggregation Techniques. MDAI 2020: 53-65 - [c11]Andrea Campagner, Luca Maria Sconfienza, Federico Cabitza:
H-Accuracy, an Alternative Metric to Assess Classification Models in Medicine. MIE 2020: 242-246 - [c10]Andrea Campagner, Federico Cabitza:
Introducing New Measures of Inter- and Intra-Rater Agreement to Assess the Reliability of Medical Ground Truth. MIE 2020: 282-286 - [c9]Andrea Campagner, Davide Ciucci, Valentina Dorigatti:
Approximate Reaction Systems Based on Rough Set Theory. IJCRS 2020: 48-60 - [c8]Andrea Campagner, Federico Cabitza, Davide Ciucci:
Three-Way Decision for Handling Uncertainty in Machine Learning: A Narrative Review. IJCRS 2020: 137-152 - [c7]Andrea Campagner, Davide Ciucci:
A Formal Learning Theory for Three-Way Clustering. SUM 2020: 128-140
2010 – 2019
- 2019
- [j1]Andrea Campagner, Davide Ciucci:
Orthopartitions and soft clustering: Soft mutual information measures for clustering validation. Knowl. Based Syst. 180: 51-61 (2019) - [c6]Andrea Campagner, Federico Cabitza, Davide Ciucci:
Exploring Medical Data Classification with Three-Way Decision Trees. HEALTHINF 2019: 147-158 - [c5]Federico Cabitza, Andrea Campagner, Davide Ciucci:
New Frontiers in Explainable AI: Understanding the GI to Interpret the GO. CD-MAKE 2019: 27-47 - [c4]Federico Cabitza, Andrea Campagner, Davide Ciucci, Andrea Seveso:
Programmed Inefficiencies in DSS-Supported Human Decision Making. MDAI 2019: 201-212 - [c3]Andrea Campagner, Federico Cabitza, Davide Ciucci:
Three-Way Classification: Ambiguity and Abstention in Machine Learning. IJCRS 2019: 280-294 - [i1]Federico Cabitza, Andrea Campagner:
Who wants accurate models? Arguing for a different metrics to take classification models seriously. CoRR abs/1910.09246 (2019) - 2018
- [c2]Andrea Campagner, Davide Ciucci:
Three-Way and Semi-supervised Decision Tree Learning Based on Orthopartitions. IPMU (2) 2018: 748-759 - 2017
- [c1]Andrea Campagner, Davide Ciucci:
Measuring Uncertainty in Orthopairs. ECSQARU 2017: 423-432
Coauthor Index
[j37] [j36] [j35] [j33] [j31] [j30] [c45] [c44] [c43] [j29] [j28] [j27] [j25] [j23] [c42] [c41] [c40] [c39] [c38] [c37] [c36] [c35] [c33] [c32] [c30] [e2] [j22] [j21] [j18] [c28] [c27] [c24] [c23] [c22] [i4] [i3] [j16] [j15] [j14] [j13] [j11] [j10] [j9] [j8] [c19] [c18] [c16] [i2] [j7] [j6] [j4] [j3] [j2] [c14] [c12] [c11] [c10] [c8] [c6] [c5] [c4] [c3] [i1]
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