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 …

We describe the two most commonly used systems for induction of decision trees for
classification: C4. 5 and CART. We highlight the methods and different decisions made in …

This article provides a birds-eye view on the role of decision trees in machine learning and
data science over roughly four decades. It sketches the evolution of decision tree research …

Decision trees have proved to be valuable tools for the description, classification and
generalization of data. Work on constructing decision trees from data exists in multiple …

Data Mining has been successful in a wide variety of application areas for varied purposes.
Data Mining itself is done using several different methods. Decision Trees are one of the …

Classification of large datasets is an important data mining problem. Many classification
algorithms have been proposed in the literature, but studies have shown that so far no …

In the previous chapter we introduced the task of proactive data mining and sketched an
algorithmic framework for solving the task: first build a prediction model and then use it for …

The Top-Down Induction of Decision Trees (TDIDT) algorithm described in previous
chapters is one of the most commonly used methods of classification. It is well known, widely …

On growing better decision trees from data On growing better decision trees from data Abstract
This thesis investigates the problem of growing decision trees from data, for the purposes of …

Classification and regression are defined under the umbrella of the prediction task of data
mining. Discrete values are predicted using classification techniques whereas regression …