Classification is one of the most researched questions in machine learning and data mining.
A wide range of real problems have been stated as classification problems, for example …

This paper presents a survey of evolutionary algorithms that are designed for decision-tree
induction. In this context, most of the paper focuses on approaches that evolve decision …

With the rapid growth of databases in many modern enterprises data mining has become an
increasingly important approach for data analysis. The operations research community has …

The world around us is changing very fast mainly because of spectacular progress in
information and communication technologies. A phenomenon known only from science …

Understanding heterogeneous multivariate time series data is important in many
applications ranging from smart homes to aviation. Learning models of heterogeneous …

Classification is a supervised machine learning process that categories an instance based
on a number of features. The process of classification involves several stages, including …

An Indexed Bibliography of Genetic Algorithms in Economics Page 1 An Indexed Bibliography
of Genetic Algorithms in Economics compiled by Jarmo T. Alander Department of Electrical …

Learning classifier systems tend to inherit—a priori—a given knowledge representation
language for expressing the concepts to learn. Hence, even before getting started, this …

Decision tree induction algorithms represent one of the most popular techniques for dealing
with classification problems. However, traditional decision-tree induction algorithms …

In machine learning, decision trees (DTs) are usually considered comprehensible because
a reasoning process can be given for each conclusion. When the data set is large, however …