Abstraction in Machine Learning

As in other fields of Artificial Intelligence, abstraction plays a key role in learning. This chapter presents the role and impact of abstraction in two much studied paradigms of Machine Learning: Learning from examples and Learning from reinforcement. After a brief introduction to these two paradigms formulated in the KRA model, a state of the art of the use of abstraction is given for each one of them. In the former, the most widely used abstraction approaches are feature selection and feature discretization, and they are exemplified on a very simple task, and R programs are given as possible operationalization of the abstraction. The Filter, Wrapper and Embedded approaches, used for feature selection, can be extended to include many other types of abstractions, in both propositional and relational learning. Feature construction, Predicate invention, Term abstraction and propositionalization are also reviewed within the context of propositional and relational learning. In the case of Reinforcement Learning, abstraction methods can be either model driven (by analyzing the transition table and approximating it using a dynamic Bayesian network), or value driven (by analyzing the function V, and learning for it a compact representation, such as a decision tree), or policy driven. These different abstractions formulated in the KRA model support the possibility of an automatic and systematic exploration of representation changes in learning.