Knowledge Discovery through SysFor - a Systematically Developed Forest of Multiple Decision Trees
Decision tree based classifications algorithms like C4.5 and explore build a single tree from a data set. The two main purposes of building a decision tree are to extract various patterns/logic-rules existing in a data set and to predict the class attributes value of an unlabeled record. Sometimes a set of decision trees, rather than just a single tree, is also generated from a data set. A set of multiple trees, when used wisely, typically have better prediction accuracy on unlabeled records. Existing multiple tree techniques is catered for high dimensional data sets and therefore unable to build many trees from low dimensional data sets.