Generally speaking, the typical task of a concept learning algorithm is as follows. Given a set of concepts and a set of examples which are said to represent these concepts, try to obtain a set of concept recognition rules (a theory). Each example given to the learning algorithm is previously classified as being an example of a specific concept. So the task of the learning algorithm is to obtain a general concept description for each of the concepts. A concept description is a set of concept recognition rules which can be included in some kind of expert system shell. These rules can then be used to classify new examples into one of the learned concepts.
When the examples are not available at the same time an incremental strategy is needed. The main motivation for that is efficiency as those systems need only to make small changes to the previously learned theory when a new example becomes available. These small changes need to be validated against the past empirical experience. For that reason incremental learning algorithms adopt a full-memory approach. With this approch all examples are retained in memory so that validation is possible. In the following sections we present some problems of this approach which lead to the need of forgetting during learning.
The next section is a brief introduction to concept learning both incremental and non-incremental. Following we discuss the notion of forgetting. Section 4 presents the idea of knowledge integration (KI) together with some experimental results. Finally we show how KI can be related to the notion of forgetting in two learning scenarios.