My Account Log in

1 option

Modeling Decisions for Artificial Intelligence : 18th International Conference, MDAI 2021, Umeå, Sweden, September 27-30, 2021, Proceedings / edited by Vicenç Torra, Yasuo Narukawa.

SpringerLink Books Computer Science (2011-2024) Available online

View online
Format:
Book
Contributor:
Torra, Vicenç, Editor.
Narukawa, Yasuo, Editor.
SpringerLink (Online service)
Series:
Computer Science (SpringerNature-11645)
Lecture notes in computer science. Lecture notes in artificial intelligence ; 12898
Lecture Notes in Artificial Intelligence ; 12898
Language:
English
Subjects (All):
Artificial intelligence.
Machine theory.
Data mining.
Application software.
Artificial Intelligence.
Formal Languages and Automata Theory.
Data Mining and Knowledge Discovery.
Computer and Information Systems Applications.
Local Subjects:
Artificial Intelligence.
Formal Languages and Automata Theory.
Data Mining and Knowledge Discovery.
Computer and Information Systems Applications.
Physical Description:
1 online resource (XIII, 347 pages) : 112 illustrations, 63 illustrations in color.
Edition:
1st ed. 2021.
Contained In:
Springer Nature eBook
Place of Publication:
Cham : Springer International Publishing : Imprint: Springer, 2021.
System Details:
text file PDF
Summary:
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.
Contents:
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
.
Other Format:
Printed edition:
ISBN:
978-3-030-85529-1
9783030855291
Access Restriction:
Restricted for use by site license.

The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.

Find

Home Release notes

My Account

Shelf Request an item Bookmarks Fines and fees Settings

Guides

Using the Find catalog Using Articles+ Using your account