Science & Engineering Research Support soCiety (SERSC)
Decision tree induction has gained its popularity as an effective automated method for data classification mainly because of its simple, easy-to-understand, and noise-tolerant characteristics. The induced tree reveals the most informative attributes that can best characterize training data and accurately predict classes of unseen data. Despite its predictive power, the tree structure can be overly expanded or deeply grown when the training data do not show explicit patterns. Such bushy and deep trees are difficult to comprehend and interpret by humans.