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 …

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

This survey: 1 Introduces the subject introduces Supervised Learning (SL) contrasts SL with
optimisation assumes readers are familiar with Evolutionary Algorithms (EAs) discusses …

In the data-mining field, many learning methods—such as association rules, Bayesian
networks, and induction rules (Grzymala-Busse & Stefanowski, 2001)—can handle only …

Central to computer security are detecting attacks against systems and managing computer
systems to mitigate threats to the system. Attacks exploit vulnerabilities in the system such as …

This work analyzes experimentally discretization algorithms for handling continuous
attributes in evolutionary learning. We consider a learning system that induces a set of rules …

Datasets with a large number of attributes are a difficult challenge for evolutionary learning
techniques. The recently proposed attribute list rule representation has shown to be able to …

In order to handle classification problems with real-valued attributes using discretization
algorithms it is necessary to obtain a good and reduced set of cut points in order to learn …

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 …

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 …