Induction of decision trees

Induction of Decision Trees

Global Induction of Decision Trees

Decision trees are, besides decision rules, one of the most popular forms of knowledge representation in Knowledge Discovery in Databases process (Fayyad, Piatetsky-Shapiro, Smyth & Uthurusamy, 1996) and implementations of the classical decision tree induction algorithms are included in the majority of data mining systems. A hierarchical structure of a tree-based classifier, where appropriate t...

Induction of shallow decision trees

In this paper we describe eecient algorithms that induce shallow (i.e., low depth) decision trees. A key feature of these algorithms is their ability to induce decision trees over real-valued data that have multiple branches at each node (in contrast to algorithms that use binary splits). As a special case, we describe eecient algorithms for computing the optimal partitioning of one dimensional...

Induction of Oblique Decision Trees

This paper introduces a randomized technique for partitioning examples using oblique hyperplanes. Standard decision tree techniques , such as ID3 and its descendants, partition a set of points with axis-parallel hyper-planes. Our method, by contrast, attempts to nd hyperplanes at any orientation. The purpose of this more general technique is to nd smaller but equally accurate decision trees tha...

Constructive Induction On Decision Trees

Selective induction techniques perform poorly when the features are inappropriate for the target concept. One solution is to have the learning system construct new features automatically ; unfortunately feature construction is a difficult and poorly understood problem. In this paper we present a definition of feature construction in concept learning, and offer a framework for its study based on...