Decision trees have become one of the most powerful and popular approaches in
knowledge discovery and data mining; it is the science of exploring large and complex …

Feature selection is one of the most important issues in the research fields such as system
modelling, data mining and pattern recognition. In this study, a new feature selection …

The majority of existing algorithms for learning decision trees are greedy—a tree is induced
topdown, making locally optimal decisions at each node. In most cases, however, the …

Most data-mining algorithms assume static behavior of the incoming data. In the real world,
the situation is different and most continuously collected data streams are generated by …

Decision tree induction is a simple but powerful learning and classification model. Decision
tree learning offers tools for discovery of relationships, patterns and knowledge from data in …

We are living in a world where complexity of systems created and studied by people grows
beyond all imaginable limits. Computers, their software and their networks are among the …

This paper presents a novel model for resource bounded computation based on process
algebras. Such model is called the $-calculus (cost calculus). Resource bounded …

Pattern recognition involves various human activities of great practical significance, such as
data-based bankruptcy prediction, speech/image recognition, machine fault detection and …

Finding a minimal decision tree consistent with the examples is an NP-complete problem.
Therefore, most of the existing algorithms for decision tree induction use a greedy approach …

Decision-tree algorithms provide one of the most popular methodologies for symbolic
knowledge acquisition. The resulting knowledge, a symbolic DECISION TREE along with a …