Concept Reliability in Machine Learning

Much machine learning research addresses inductive learning — learning relationships from a set of examples (Michalski (1986) provides an excellent introduction). For instance, some programs have been used to learn medical diagnostic rules from a database of patients whose diagnoses are known. These programs examine a number of attributes (e.g. age, temperature, and pulse rate) for a set of examples whose classification (e.g. diagnosis) is known. This set of examples is termed a training set. Attributes tests are combined into logical rules which are used to predict the classification (e.g. if (age > 5) and (temperature > 100), then (preliminary-diagnosis = not-normal)). These rules are generally termed concepts.