Chapter List

1.1 Introduction

In concept learning the features used to describe examples can have a drastic effect on the learning algorithm's ability to acquire the target concept. ^[1] In many poorly understood domains the representation can be described as being low level. Examples are described in terms of a large number of small measurements. No single measurement is strongly correlated to the target concept; however, all information for classification is believed to be present. Patterns are harder to identify because they are conditional. This is in contrast to problems where a small number of attributes are highly predictive of the concept. Clark and Thornton [1] call these type-2 and type-1 problems, respectively. Many current approaches perform very poorly on type-2 problems because the biases which they employ are poorly tuned to the underlying concept. In this chapter we discuss reasons why attempting to estimate concept difficulty before any learning takes place is desirable. Some current approaches to learning type-2 problems are described together with several measures used to estimate particular sources of difficulty, their advantages and disadvantages and an...