general criteria on building decision trees for data classification
Decision trees have been widely and successfully applied to data mining and machine learning for data classification. One of the critical components in a decision tree algorithm is the criterion used to select which attribute will become a test attribute in a given branch of the tree. Several algorithms, such as ID3, C4.5, and CART, are investigated and compared for determining attributes as the root and the branches in decision trees. This paper presents the issues of traditional ID3 algorithm that directly affect building decision trees. The goal is to select critical factors in ID3 algorithm that result in an efficient decision tree for classification applications. Examples are given to illustrate what factors may affect the construction of decision trees.
decision tree classification id3 algorithm information gain