Decision Tree Induction Methods and Their Application to Big Data

Abstract

Data mining methods are widely used across many disciplines to identify patterns, rules, or associations among huge volumes of data. While in the past mostly black box methods, such as neural nets and support vector machines, have been heavily used for the prediction of pattern, classes, or events, methods that have explanation capability such as decision tree induction methods are seldom preferred. Therefore, we give in this chapter an introduction to decision tree induction. The basic principle, the advantageous properties of decision tree induction methods, and a description of the representation of decision trees so that a user can understand and describe the tree in a common way is given first. The overall decision tree induction algorithm is explained as well as different methods for the most important functions of a decision tree induction algorithm, such as attribute selection, attribute discretization, and pruning, developed by us and others. We explain how the learnt model can be fitted to the expert´s knowledge and how the classification performance can be improved. The problem of feature subset selection by decision tree induction is described. The quality of the learnt model is not only to be checked based on the overall accuracy, but also more specific measure are explained that describe the performance of the model in more detail. We present a new quantitative measures that can describe changes in the structure of a tree in order to help the expert to interpret the differences of two learnt trees from the same domain. Finally, we summarize our chapter and give an outlook.