Multiway decision tree induction using projection and merging (MPEG)

Decision trees are one of the most popular and commonly used classification models. Many algorithms have been designed to build decision trees in the last twenty years. These algorithms are categorized into two groups according to the type of the trees they build, binary and multiway trees. In this paper, a new algorithm, MPEG, is designed to build multiway trees. MPEG uses DBMS indices and optimized query to access the dataset it works on, hence it has few memory requirements and no restrictions on sizes of datasets. Projection of examples over attribute values, merging of generated partitions using class values, applying GINI index to select among different attributes and finally post pruning using EBP method, are the basic steps of MPEG. The trees built by MPEG have the advantages of binary trees as being accurate, small in size and the advantages of multiway trees as being compact and easy to be comprehended by humans.