This book discusses machine learning algorithms, such as artificial neural networks of
different architectures, statistical learning theory, and Support Vector Machines used for the …

Data splitting is an important consideration during artificial neural network (ANN)
development where hold-out cross-validation is commonly employed to ensure …

Traditional bankruptcy prediction models, designed using classification or regression
techniques, achieve short-term performances (1 year) that are fairly good, but that often …

Purpose–This study aims to examine and visualize the adopted internal control structure
and effectiveness in firms and present a typology of firms. Control structure and effectiveness …

Bankruptcy prediction models that rely on ensemble techniques have been studied in depth
over the last 20 years. Within most studies that have been performed on this topic, it appears …

Most bankruptcy prediction models used by financial institutions rely on single-period data,
that is to say data that characterize firms at a given moment of their life. However, the …

For more than a decade, the number of research works that deal with ensemble methods
applied to bankruptcy prediction has been increasing. Ensemble techniques present some …

The effective modelling of high-dimensional data with hundreds to thousands of features
remains a challenging task in the field of machine learning. This process is a manually …

Abstract The Self-Organizing Maps (SOM) is a very popular algorithm, introduced by Teuvo
Kohonen in the early 80s. It acts as a non supervised clustering algorithm as well as a …

In several regions of the world, climate change is expected to have severe impacts on
agricultural systems. Changes in land management are one way to adapt to future climatic …