A New Direct Node Data Splitting Technique in Decision Tree Induction

Abstract

Learning knowledge from the example data and then applying that knowledge on new applications is the goal of machine learning. Split attribute technique is inseparable and an important means of decision tree construction technique. It is a well known fact and universally accepted truth that a difficult and larger data mining model tends to create less significant generalized performance results. Researchers are being continuously trying to find new and the best split attribute techniques during decision tree induction. In this paper a new direct split attribute technique for decision tree induction is proposed based on the mathematical A implies B tautology principle. The experimental results show that this rule is worthy and useful in many real world applications, particularly in medical field. The resulted association relationships are perfectly matching with the expected results.