Analysis of Decision Tree Induction Algorithms

Reusable components in decision tree induction algorithms

We propose a generic decision tree framework that supports reusable components design. The proposed generic decision tree framework consists of several sub-problems which were recognized by analyzing well-known decision tree induction algorithms, namely ID3, C4.5, CART, CHAID, QUEST, GUIDE, CRUISE, and CTREE. We identified reusable components in these algorithms as well as in several of their p...

Evolutionary Algorithms in Decision Tree Induction

Decision Tree Induction Systems: A Bayesian Analysis

Decision tree induction systems are being used for knowledge acquisition. Yet they have been developed without proper regard for the subjective Bayesian theory of inductive inference. This paper examines the problem tackled by these systems from the Bayesian view in order to interpret the systems and the heuristic methods they use. It is shown that decision tree systems depart from the usual Ba...

Linguistic Decision Tree Induction

Label Semantics is a random set based framework for modeling imprecise concepts where the degree of appropriateness of a linguistic expression as a description of a certain value is measured in terms of how the set of appropriate labels for that value varies across a population. An approach to decision tree induction based on this framework was studied. A new decision tree learning algorithm wa...

Decision Tree Induction

Decision Tree Induction (DTI) is an important step of the segmentation methodology. It can be viewed as a tool for the analysis of large datasets characterized by high dimensionality and nonstandard structure. Segmentation follows a nonparametric approach, since no hypotheses are made on the variable distribution. The resulting model has the structure of a tree graph. It is considered a supervi...