Recent Trends and Future Challenges in Learning from Data / edited by Cristina Davino, Francesco Palumbo, Adalbert F. X. Wilhelm, Hans A. Kestler.

Format:

Book

Contributor:

Davino, Cristina, editor.

Series:

Studies in Classification, Data Analysis, and Knowledge Organization, 2198-3321

Language:

English

Subjects (All):

Quantitative research.

Data mining.

Mathematical statistics--Data processing.

Mathematical statistics.

Statistics.

Machine learning.

Data Analysis and Big Data.

Data Mining and Knowledge Discovery.

Statistics and Computing.

Statistical Theory and Methods.

Applied Statistics.

Statistical Learning.

Local Subjects:

Data Analysis and Big Data.

Data Mining and Knowledge Discovery.

Statistics and Computing.

Statistical Theory and Methods.

Applied Statistics.

Statistical Learning.

Physical Description:

1 online resource (158 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

Summary:

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.

Contents:

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.

Notes:

Includes bibliographical references.

ISBN:

3-031-54468-4