Summary Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the
process of neo-Darwinian evolution. The motivation for applying EAs to data mining is that …

A discretization algorithm is needed in order to handle problems with real-valued attributes
with Decision Trees (DTs), Bayesian Networks (BNs) and Rule-Learners (RLs), treating the …

Nowadays computers are used to solve incredibly complex problems. But in order to
manage with a problem we should develop an algorithm. Sometimes the human brain is not …

Evolutionary learning techniques are comparable in accuracy with other learning methods
such as Bayesian Learning, SVM, etc. These techniques often produce more interpretable …

This paper presents an overview of evolutionary approaches to Inductive Logic
Programming (ILP). After a short description of the two popular ILP systems FOIL and Progol …

The paper presents the possible applications of using acoustic diagnostics in inspecting the
technical condition of an internal combustion engine with autoignition on the example of the …

The paper is a continuation of the publication under the title “Acoustic diagnostics
applications in the study of technical condition of combustion engine” and concerns the …

Discretization is a preprocessing task which when conducted leads to very good results in
declaring the rules between attributed, classifications of objects and predicting of classes …

Prediction and classification of medical datasets help in reducing the number of diagnostic
procedure to identify the diseases there by providing economical solutions for healthcare …

An important characteristic of all natural systems is the ability to acquire knowledge through
experience and to adapt to new situations. Learning is the single unifying theme of all …