Michelangelo Ceci

Jiming Liu

George A. Papadopoulos

Nathalie Japkowicz

Zbigniew W. Raś

This book constitutes the proceedings of the 24th International Symposium on Foundations of Intelligent Systems, ISMIS 2018, held in Limassol, Cyprus, in October 2018. The 32 full, 8 short, and 4 application papers presented in this volume were carefully reviewed and selected from 59 submissions. The papers deal with topics such as bioinformatics and health informatics, graph mining, image analysis, intelligent systems, mining complex patterns, novelty detection and class imbalance, social data analysis, spatio-temporal analysis, and topic modeling and opinion mining. In addition, three special sessions were organized, namely: Special Session on Granular and Soft Clustering for Data Science, Special Session on Intelligent Methodologies for Traffic Data Analysis and Mining, and Special Session on Advanced Methods in Machine Learning for Modeling Complex Data.

24th International Symposium, ISMIS 2018, Limassol, Cyprus, October 29–31, 2018, Proceedings

Foundations of Intelligent Systems

Mohak Shah

The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.

A Classification Perspective

Evaluating Learning Algorithms

Jerzy Stefanowski

This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area. It demonstrates that Big Data Analysis opens up new research problems which were either never considered before, or were only considered within a limited range. In addition to providing methodological discussions on the principles of mining Big Data and the difference between traditional statistical data analysis and newer computing frameworks, this book presents recently developed algorithms affecting such areas as business, financial forecasting, human mobility, the Internet of Things, information networks, bioinformatics, medical systems and life science. It explores, through a number of specific examples, how the study of Big Data Analysis has evolved and how it has started and will most likely continue to affect society. While the benefits brought upon by Big Data Analysis are underlined, the book also discusses some of the warnings that have been issued concerning the potential dangers of Big Data Analysis along with its pitfalls and challenges.

Big Data Analysis: New Algorithms for a New Society

Nathalie Japkowicz

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