An empirical study on the incompetence of attribute selection criteria

摘要

One of the main tasks in most supervised learning systems is the evaluation of the attributional relevancy in the given databases. Such relevancy is mainly concerned with the relationship between the available attributes and the decision classes. Attributes relevant to the decision classes are used to represent the learned knowledge, while irrelevant attributes are removed or ignored during the learning process. This paper investigates the relationship between attributional relevancy to decision classes and to learning systems. The experimental results from different databases show that some attributes relevant to decision classes may be irrelevant to the learning system. Experiments are performed on eight different databases using the C4.5 system for learning decision trees from examples.