Decision Tree Induction Using Rough Set Theory - Comparative Study
Dimensional reduction has been a major problem in data mining problems. In many real time situations, e.g. database applications and bioinformatics, there are far too many attributes to be handled by learning schemes, majority of them being redundant. Taking predominant attributes reduces the dimensions of the data, which in turn reduces the size of the hypothesis space, allowing classification algorithm to operate faster and more efficiently. The Rough Set (RS) theory is one such approach for dimension reduction. RS offers a simplified search for predominant attributes in datasets. Rough Set based Decision Tree (RDT) is constructed based on the predominant attributes. The comparative analysis with the existing decision tree algorithms was made to show that the intent of RDT is to improve efficiency, simplicity and generalization capability.