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Recent trends and future challenges in learning from data

Record Type:	Electronic resources : Monograph/item
Title/Author:	Recent trends and future challenges in learning from dataedited by Cristina Davino ...[et al.].
other author:	Davino, Cristina.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	x, 153 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Data miningCongresses.
Online resource:	https://doi.org/10.1007/978-3-031-54468-2
ISBN:	9783031544682$q(electronic bk.)

Recent trends and future challenges in learning from data
Recent trends and future challenges in learning from data[electronic resource] /edited by Cristina Davino ...[et al.]. - Cham :Springer Nature Switzerland :2024. - x, 153 p. :ill. (some col.), digital ;24 cm. - Studies in classification, data analysis, and knowledge organization,2198-3321. - Studies in classification, data analysis, and knowledge organization..

Preface -- Building hierarchies of factors with disjoint factor analysis -- Uncertainty in Latent Trait Models and dimensionality reduction methods for complex data: an analysis of taxpayer perception on the Fiscal System -- The predictivity of access tests for university success -- Asynchronous and synchronous-asynchronous particle swarms -- The impact of the Covid-19 pandemic on modelling volatility and risk analysis of returns in selected European financial markets -- Asymmetric binary regression models for imbalanced datasets: an application to students' churn -- Computational models supporting decision-making in managing publication activity at Polish universities -- Stability of nonparametric methods for cognitive diagnostic assessment -- SMARTS: SeMi-supervised clustering for Assessment of Reviews using Topic and Sentiment -- The equitable and sustainable wellbeing through the pandemic. A first study to assess changes at local level in Italy -- Choice-Based Optimization under High-Dimensional MNL -- A first glance on co-evolution of Boolean networks to simulate the development of cross-talking systems in molecular biology -- Classification on polish fund market during COVID-19 pandemic - extreme risk modeling approach.

This book collects together selected peer-reviewed contributions presented at the European Conference on Data Analysis, ECDA 2022, held in Naples, Italy, September 14-16, 2022. Highlighting the role of statistics in discovering novel and interesting patterns in the era of big data, it follows the motto of the conference: "Avoiding drowning in the data: recent trends and future challenges in learning from data". The central focus is on multidisciplinary approaches to data analysis, classification, and the interface between computer science, data mining and statistics. Both methodological and applied topics are covered. The former includes supervised and unsupervised techniques with particular emphasis on advances in regression and clustering analysis and constructing composite indicators. The applications are mainly in risk analysis, biology, and education. The volume is organized into four main macro themes: methodological contributions in the social sciences and education, multivariate analysis methods for big data, innovative contributions for applications inspired by biology, and strategies for analyzing complex data in finance.

ISBN: 9783031544682$q(electronic bk.)

Standard No.: 10.1007/978-3-031-54468-2doiSubjects--Topical Terms:

380776
Data mining
--Congresses.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Recent trends and future challenges in learning from data
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520 $a This book collects together selected peer-reviewed contributions presented at the European Conference on Data Analysis, ECDA 2022, held in Naples, Italy, September 14-16, 2022. Highlighting the role of statistics in discovering novel and interesting patterns in the era of big data, it follows the motto of the conference: "Avoiding drowning in the data: recent trends and future challenges in learning from data". The central focus is on multidisciplinary approaches to data analysis, classification, and the interface between computer science, data mining and statistics. Both methodological and applied topics are covered. The former includes supervised and unsupervised techniques with particular emphasis on advances in regression and clustering analysis and constructing composite indicators. The applications are mainly in risk analysis, biology, and education. The volume is organized into four main macro themes: methodological contributions in the social sciences and education, multivariate analysis methods for big data, innovative contributions for applications inspired by biology, and strategies for analyzing complex data in finance.
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