Data science, classification, and artificial intelligence for modeling decision making

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Material type: Text

TextSeries: Studies in Classification, Data Analysis, and Knowledge Organization (STUDIES CLASS)Publication details: Cham Springer 2025Description: x, 192 pISBN:

9783031858697

Subject(s):

DDC classification:

006.312 TRE

Summary: This book gathers selected and peer-reviewed contributions presented at the 18th Conference of the International Federation of Classification Societies (IFCS 2024), held in San José, Costa Rica, July 15–19, 2024. Covering a wide range of topics, it describes modern methods and real-world applications in data science, classification, and artificial intelligence related to modeling decision making. Numerous novel techniques and innovative applications are investigated, such as anomaly detection in public procurement processes, multivariate functional data clustering, air pollution prediction, benchmark generation for probabilistic planning, recommendation systems based on symbolic data analysis, and methods for clustering mixed-type data. Advanced statistical concepts are explored, including Vapnik-Chervonenkis dimensionality, Riemannian statistics, hypothesis testing for interval-valued data, and mixed models. Furthermore, machine learning techniques are applied to predict soil bacterial and fungal communities, classify electoral behavior and political competition, and assess corrosion degradation in mining pipelines. The diversity of topics discussed in this collection reflects the ongoing advancement and interdisciplinary nature of statistical and data science research, as well as its application across various fields and sectors. These studies contribute to the development of robust methodologies and efficient computational tools to address complex challenges in the era of big data. The book is intended for researchers and practitioners seeking the latest developments and applications in the field of data science and classification. (https://link.springer.com/book/10.1007/978-3-031-85870-3)

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	006.312 TRE (Browse shelf(Opens below))	1	Available		009185

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Previous	006.312 PRA Introduction to data governance for machine learning systems: fundamental principles, critical practices, and future trends	006.312 SIM Mathematical analysis for machine learning and data mining	006.312 TAN Introduction to data mining	006.312 TRE Data science, classification, and artificial intelligence for modeling decision making	006.312 ZAF Social media mining: an introduction	006.312 ZAK Data mining and machine learning:	006.312 ZHA A tour of data science:	Next

Table of contents:
Front Matter
Pages i-xii
Download chapter PDF
A Comparison of Multivariate Mixed Models and Generalized Estimation Equations Models for Discrimination in Multivariate Longitudinal Data
Gabriel Afriyie, David M. Hughes, Alberto Nettel Aguirre, Na Li, Chel Hee Lee, Lisa M. Lix et al.
Pages 3-13
A Multivariate Functional Data Clustering Method Using Parsimonious Cluster Weighted Models
Cristina Adela Anton, Iain Smith
Pages 15-22
Unsupervised Detection of Anomaly in Public Procurement Processes
Jose Pablo Arroyo-Castro, Shu Wei Chou-Chen
Pages 23-32
Predicting Soil Bacterial and Fungal Communities at Different Taxonomic Levels Using Machine Learning
Zahia Aouabed, Mohamed Achraf Bouaoune, Vincent Therrien, Mohammadreza Bakhtyari, Mohamed Hijri, Vladimir Makarenkov
Pages 33-41
Candidates, Parties, Issues and the Political Marketing Strategies: A Comparative Analysis on Political Competition in Greece
Vasiliki Bouranta, Georgia Panagiotidou, Theodore Chadjipadelis
Pages 43-52
Predicting Air Pollution in Beijing, China Using Chemical, and Climate Variables
Joshua Cervantes, Moisés Monge, Daniel Sabater
Pages 53-60
Towards Topologically Diverse Probabilistic Planning Benchmarks: Synthetic Domain Generation for Markov Decision Processes
Jaël Champagne Gareau, Éric Beaudry, Vladimir Makarenkov
Pages 61-69
Symbolic Data Analysis Framework for Recommendation Systems: SDA-RecSys
Pushya Chaparala, Panduranganaidu Nagabhushan
Pages 71-79
A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data
Efthymios Costa, Ioanna Papatsouma, Angelos Markos
Pages 81-88
A New Metric to Classify B Cell Lineage Tree
Mahsa Farnia, Nadia Tahiri
Pages 89-97
Applying Classification Methods for Multivariate Functional Data
Tomasz Górecki, Miroslaw Krzyśko, Waldemar Wolyński
Pages 99-105
Machine Learning-Based Classification and Prediction to Assess Corrosion Degradation in Mining Pipelines
Kalidou Moussa Sow, Nadia Ghazzali
Pages 107-114
Modelling Clusters in Network Time Series with an Application to Presidential Elections in the USA
Guy Nason, Daniel Salnikov, Mario Cortina-Borja
Pages 115-123
On the Vapnik-Chervonenkis Dimension and Learnability of the Hurwicz Decision Criterion
Manuel A. Nunez, Mark A. Schneider
Pages 125-132
Distributional-based Partitioning with Copulas
Wenhao Pan, Lynne Billard
Pages 133-140
Mapping Electoral Behavior and Political Competition: A Comparative Analytical Framework for Voter Typologies and Political Discourses
Georgia Panagiotidou, Theodore Chadjipadelis
Pages 141-150
Riemannian Statistics for Any Type of Data
Oldemar Rodríguez Rojas
Pages 151-160
Hypothesis Testing of Mean Interval for p-Dimensional Interval-Valued Data
Anuradha Roy, Fernando Montes
Pages 161-169
UMAP Projections and the Survival of Empty Space: A Geometric Approach to High-Dimensional Data
Maikol Solís, Alberto Hernández
Pages 171-179
An Efficient Multicore CPU Implementation of the Databionic Swarm
Quirin Stier, Michael C. Thrun
Pages 181-190
Back Matter
Pages 191-192
An Efficient Multicore CPU Implementation of the Databionic Swarm
Quirin Stier, Michael C. Thrun
Pages 181-190
Back Matter
Pages 191-192

[https://link.springer.com/book/10.1007/978-3-031-85870-3?page=2#toc]

This book gathers selected and peer-reviewed contributions presented at the 18th Conference of the International Federation of Classification Societies (IFCS 2024), held in San José, Costa Rica, July 15–19, 2024. Covering a wide range of topics, it describes modern methods and real-world applications in data science, classification, and artificial intelligence related to modeling decision making.

Numerous novel techniques and innovative applications are investigated, such as anomaly detection in public procurement processes, multivariate functional data clustering, air pollution prediction, benchmark generation for probabilistic planning, recommendation systems based on symbolic data analysis, and methods for clustering mixed-type data. Advanced statistical concepts are explored, including Vapnik-Chervonenkis dimensionality, Riemannian statistics, hypothesis testing for interval-valued data, and mixed models. Furthermore, machine learning techniques are applied to predict soil bacterial and fungal communities, classify electoral behavior and political competition, and assess corrosion degradation in mining pipelines.

The diversity of topics discussed in this collection reflects the ongoing advancement and interdisciplinary nature of statistical and data science research, as well as its application across various fields and sectors. These studies contribute to the development of robust methodologies and efficient computational tools to address complex challenges in the era of big data.

The book is intended for researchers and practitioners seeking the latest developments and applications in the field of data science and classification.

(https://link.springer.com/book/10.1007/978-3-031-85870-3)

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