In recent two decades, artificial neural networks have been extensively used in many
business applications. Despite the growing number of research papers, only few studies …

This paper presents a comprehensive review of the work done, during the 1968–2005, in the
application of statistical and intelligent techniques to solve the bankruptcy prediction …

For financial institutions, the ability to predict or forecast business failures is crucial, as
incorrect decisions can have direct financial consequences. Bankruptcy prediction and …

Novel non-parametric dimensionality reduction techniques such as t-distributed stochastic
neighbor embedding (t-SNE) lead to a powerful and flexible visualization of high …

The goal of this study is to show an alternative method to corporate failure prediction. In the
last decades Artificial Neural Networks have been widely used for this task. These models …

We propose a new scheme for enlarging generalized learning vector quantization (GLVQ)
with weighting factors for the input dimensions. The factors allow an appropriate scaling of …

Nonlinear dimensionality reduction methods are often used to visualize high-dimensional
data, although the existing methods have been designed for other related tasks such as …

Abstract The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest
among researches and practitioners in a wide variety of fields. The SOM has been analyzed …

Background Conventionally, the first step in analyzing the large and high-dimensional data
sets measured by microarrays is visual exploration. Dendrograms of hierarchical clustering …

Purpose Corporate failure remains a critical financial concern, with implications for both
firms and financial institutions; this paper aims to review the literature that proposes …