Decision tree building:

The problem is how to construct a decision tree, how to infer structure and classification rules from a data set. Most of the construction algorithms are referred to as Top-Down Induction of Decision Trees (TDIDT) [45]. Induction, because the knowledge is acquired inductively from the data-set; top-down because a candidate rule is chosen first for the single top decision node and then each of its subsets is recursively partitioned. The splitting is terminated if all members of a subset belong to the same class or no further decision criteria are left. Some newer algorithms also stop splitting, referred to as ``pre pruning'' the tree, if the classification improvement seems insignificant. Other algorithms replace insignificant subtrees by leafs in a post process, known as ``post pruning''.