The Irrelevant Values Problem of Decision Tree for Improving a Glass Sputtering Process

[[abstract]]In this paper, we use decision tree to establish a yield improvement model for glass sputtering process; however, the tree may have irrelevant values problem. In other words, when the tree is represented by a set of rules, not only comprehensibility of the resultant rules will be detracted but also critical factors of the manufacturing process cannot be effectively identified. From the performance issue and practical issue, we have to remove irrelevant conditions from the rules; otherwise, a domain expert is needed to review the decision tree. In this paper, we use a very simple example to demonstrate this point of view. Moreover, to identify and remove irrelevant conditions from the rules, we also revise Chiang's previous algorithm such that the modified algorithm can deal not only discrete data but also quantitative data.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙

THE IRRELEVANT VALUES PROBLEM IN THE ID3 TREE

When a decision tree is represented by a collection of rules, the antecedents of individual rules may contain irrelevant conditions. To avoid generating rules with irrelevant conditions, we propose a new algorithm to remove irrelevant conditions of rules in the process of converting the decision tree to rules according to information on the decision tree. Since irrelevant conditions are removed from the resultant rules, the resultant rules are more general than those represented by the decision tree. As a side effect, the resultant rules are less likely to suffer from missing branches