Modeling Decisions for Artificial Intelligence
Description
This book constitutes the refereed proceedings of the 18th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2021, held in Umeå, Sweden, in September 2021.*
The 24 papers presented in this volume were carefully reviewed and selected from 50 submissions. Additionally, 3 invited papers were included. The papers discuss different facets of decision processes in a broad sense and present research in data science, data privacy, aggregation functions, human decision making, graphs and social networks, and recommendation and search. The papers are organized in the following topical sections: aggregation operators and decision making; approximate reasoning; machine learning; data science and data privacy.
*The conference was held virtually due to the COVID-19 pandemic.
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Invited Papers.- Andness-Directed Iterative OWA Aggregators.- New Eliahou semigroups and verification of the Wilf conjecture for genus up to 65.- Are Sequential Patterns Shareable? Ensuring Individuals' Privacy.- Aggregation Operators and Decision Making.- On Two Generalizations for k-additivity.- Sequential decision-making using hybrid probability-possibility functions.- Numerical comparison of idempotent andness-directed aggregators.- Approximate Reasoning.- Multiple testing of conditional independence hypotheses using information-theoretic approach.- A Bayesian Interpretation of the Monty Hall Problem with Epistemic Uncertainty.- How the F-transform can be defined for hesitant, soft or intuitionistic fuzzy sets? Enhancing social recommenders with implicit preferences and fuzzy confidence functions.- A Necessity Measure of Fuzzy Inclusion Relation in Linear Programming Problems.- Machine Learning.- Mass-based Similarity Weighted k-Neighbor for Class Imbalance.- Multinomial-based Decision Synthesis of ML Classification Outputs.- Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems.- Evidential undersampling approach for imbalanced datasets with class-overlapping and noise.- Well-Calibrated and Sharp Interpretable Multi-Class Models.- Automated Attribute Weighting Fuzzy k-Centers Algorithm for Categorical Data Clustering.- q-Divergence Regularization of Bezdek-Type Fuzzy Clustering for Categorical Multivariate Data.- Automatic Clustering of CT Scans of COVID-19 Patients Based on Deep Learning.- Network Clustering with Controlled Node Size.- Data Science and Data Privacy.- Fair-ly Private Through Group Tagging and Relation Impact.- MEDICI: A simple to use synthetic social network data generator.- Answer Passage Ranking Enhancement Using Shallow Linguistic Features.- Neural embedded Dirichlet Processes for topic modeling.- Density-Based Evaluation Metrics in Unsupervised Anomaly Detection Contexts.- Explaining Image Misclassification in Deep Learning via Adversarial Examples.-Towards Machine Learning-Assisted Output Checking for Statistical Disclosure Control.-