The automatic induction of machine learning models capable of addressing supervised
learning, feature selection, clustering and reinforcement learning problems requires …

This paper investigates the feature subset selection problem for the binary classification
problem using logistic regression model. We developed a modified discrete particle swarm …

Machine learning for stock market prediction has recently been popular for identifying stock
selection strategies and providing market insights. In this study, we adopted machine …

This paper presents the methodological assumptions of a logistic regression model and
presents an example of the use of this type of models to assess the factors determining …

This proposed text appears to be a good introduction to evolutionary computation for use in
applied statistics research. The authors draw from a vast base of knowledge about the …

The aim of this study was to identify and assess the factors influencing the increase in the
financial energy of a farm through the use of external capital, taking into account the farmer's …

Objectives: The “large k (genes), small N (samples)” phenomenon complicates the problem
of microarray classification with logistic regression. The indeterminacy of the maximum …

The purpose of this study is to assess model risk with respect to parameter estimation for a
simple binary logistic regression model applied as a predictive model. The assessment is …

In E. coli, Z-ring formation precedes the assembly of the membrane that partitions a cell into
two daughter cells. Initially, as FtsZ proteins are expressed, they preferentially locate at the …

Among the many contributions made by information technologies to Bioinformatics, the
techniques of intelligent data analysis combined with optimization techniques are the main …