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 …

The induction of decision trees is a widely-used approach to build classification models that
guarantee high performance and expressiveness. Since a recursive-partitioning strategy …

Classification is a frequently encountered data mining problem. Decision tree techniques
have been widely used to build classification models as such models closely resemble …

For supervised classification problems involving design, control, and other practical
purposes, users are not only interested in finding a highly accurate classifier but they also …

The knowledge of the likely problematic areas of a software system is very useful for
improving its overall quality. Based on such information, a more focused software testing …

Decision trees are widely disseminated as an effective solution for classification tasks.
Decision tree induction algorithms have some limitations though, due to the typical strategy …

Among the several tasks that evolutionary algorithms have successfully employed, the
induction of classification rules and decision trees has been shown to be a relevant …

È É Ê ËÌ# Í# h Φ ËÊ Ãnv¥ fpr yv XtrvF£ rS" efw epy vwu r" rf § q fª© 0 tpuwr dr" u h5 r"«£" grS|
vg pu0 s trv¦ vr" uu rS" p¡ vg § Ïr"¥ f¦ rS s} Su r|© ªs § ÐrS¡ Âtp X£ sX} upuX} q© t£ § Ñr" Xuw¥ …

For pattern recognition, decision trees (DTs) are more efficient than neural networks (NNs)
for two reasons. First, the computations in making decisions are simpler. Second, important …