Torra / Narukawa Modeling Decisions for Artificial Intelligence

19th International Conference, MDAI 2022, Sant Cugat, Spain, August 30 – September 2, 2022, Proceedings
1. Auflage 2022
ISBN: 978-3-031-13448-7
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 13408, 203 Seiten, eBook

Reihe: Lecture Notes in Computer Science

This book constitutes the refereed proceedings of the 19th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2022, held in Sant Cugat, Spain, during August - September 2022. The 16 papers presented in this volume were carefully reviewed and selected from 41 submissions.  The papers discuss different facets of decision processes in a broad sense and present research in data science, machine learning, data privacy, aggregation functions, human decision-making, graphs and social networks, and recommendation and search. They were organized in topical sections as follows: Decision making and uncertainty; Data privacy; Machine Learning and data science.

Zielgruppe

Research

Autoren/Hrsg.

Torra, Vicenç

Narukawa, Yasuo

Inhaltsverzeichnis

Decision making and uncertainty.-  Optimality Analysis for Stochastic LP Problems.- A Multi-Perceptual-Based Approach for Group Decision Aiding.- Probabilistic Judgement Aggregation by Opinion Update.- Semiring-valued fuzzy rough sets and colour segmentation.- Data privacy.- Bistochastic privacy.- Improvement of Estimate Distribution with Local Differential Privacy.- Geolocated Data Generation and Protection Using Generative Adversarial Net-works.-  Machine Learning and data science.-  A Strategic Approach based on AND-OR Recommendation Trees for Updating Obsolete Information.- Identification of Subjects Wearing a Surgical Mask from their Speech by means of x-vectors and Fisher Vectors.- Measuring Fairness in Machine Learning models via Counterfactual Examples.- Re-Calibrating Machine Learning Models using Confidence Interval Bounds.- An Analysis of Byzantine-Tolerant Aggregation Mechanisms on Model Poisoning in Federated Learning.- Effective Early Stopping of Point Cloud Neural Networks.- Representation and Interpretability of IE Integral Neural Networks.- Deep Attributed Graph Embeddings.- Estimation of Prediction Error with Regression Trees.