Rule Selection Method in Decision Tree Models

Abstract

Data mining is a process of discovering useful patterns or information from large amount of data. Decision tree is one of the data mining algorithms that can be used for both classification and prediction and has been widely used for various applications because of its flexibility and interpretability. Decision trees for classification generally generate a number of rules that belong to one of the predefined category and some rules may belong to the same category. In this case, it is necessary to determine the significance of each rule so as to provide the priority of the rule with users. The purpose of this paper is to propose a rule selection method in classification tree models that accommodate the umber of observation, accuracy, and effectiveness in each rule. Our experiments demonstrate that the proposed method produce better performance compared to other existing rule selection methods. †

Optimal Deformation of a Small Plastic Light-guide Using Machine Learning Algorithms

“…We used RapidMiner Studio Version Educational 9.10 to create a Decision tree model. The advantage of the Decision tree is that due to its high explanatory power, it is easy to grasp which variables influence the predicted value and are relatively less sensitive to outliers than other machine learning models [29]. Figure 11 briefly describes the process from data input to performance evaluation.…”